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Add custom nodes, Civitai loras (LFS), and vast.ai setup script
Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-09 00:56:42 +00:00

112 lines
2.2 KiB
Python

from ..core import BOOLEAN, CATEGORY, STRING, INT, FLOAT, STRING_ML
class CBoolean:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"boolean": BOOLEAN,
}
}
CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value
RETURN_TYPES = ("BOOLEAN",)
RETURN_NAMES = ("boolean",)
FUNCTION = "execute"
def execute(self, boolean=True):
return (boolean,)
class CText:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"string": STRING,
}
}
CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("string",)
FUNCTION = "execute"
def execute(self, string=""):
return (string,)
class CTextML:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"string": STRING_ML,
}
}
CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("string",)
FUNCTION = "execute"
def execute(self, string=""):
return (string,)
class CInteger:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"int": INT,
}
}
CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value
RETURN_TYPES = ("INT",)
RETURN_NAMES = ("int",)
FUNCTION = "execute"
def execute(self, int=True):
return (int,)
class CFloat:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"float": FLOAT,
}
}
CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value
RETURN_TYPES = ("FLOAT",)
RETURN_NAMES = ("float",)
FUNCTION = "execute"
def execute(self, float=True):
return (float,)