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>

custom_nodes/sd-perturbed-attention/pladis_nodes.py

@@ -0,0 +1,83 @@
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from comfy.ldm.modules.attention import BasicTransformerBlock
+from comfy.model_base import BaseModel
+from comfy.model_patcher import ModelPatcher
+
+from .guidance_utils import parse_unet_blocks
+from .pladis_utils import SPARSE_FUNCTIONS, pladis_attention_wrapper
+
+
+class Pladis(ComfyNodeABC):
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "model": (IO.MODEL, {}),
+                "scale": (IO.FLOAT, {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                "sparse_func": (IO.COMBO, {"default": SPARSE_FUNCTIONS[0], "options": SPARSE_FUNCTIONS}),
+            },
+            "optional": {
+                "unet_block_list": (
+                    IO.STRING,
+                    {
+                        "default": "",
+                        "tooltip": (
+                            "Comma-separated blocks to which Pladis is being applied to. When the list is empty, PLADIS is being applied to all `u` and `d` blocks.\n"
+                            "Read README from sd-perturbed-attention for more details."
+                        ),
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = (IO.MODEL,)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/unet"
+    EXPERIMENTAL = True
+
+    def patch(
+        self,
+        model: ModelPatcher,
+        scale=2.0,
+        sparse_func=SPARSE_FUNCTIONS[0],
+        unet_block_list="",
+    ):
+        m = model.clone()
+        inner_model: BaseModel = m.model
+        pladis_attention = pladis_attention_wrapper(scale, sparse_func)
+
+        blocks, block_names = parse_unet_blocks(m, unet_block_list, "attn2") if unet_block_list else (None, None)
+
+        # Apply PLADIS only to transformer blocks with cross-attention (attn2)
+        for name, module in (
+            (n, m)
+            for n, m in inner_model.diffusion_model.named_modules()
+            if isinstance(m, BasicTransformerBlock) and getattr(m, "attn2", None)
+        ):
+            parts = name.split(".")
+            block_name: str = parts[0].split("_")[0]
+            block_id = int(parts[1])
+            if block_name == "middle":
+                block_id = block_id - 1
+                if not blocks:
+                    continue
+
+            t_idx = None
+            if "transformer_blocks" in parts:
+                t_pos = parts.index("transformer_blocks") + 1
+                t_idx = int(parts[t_pos])
+
+            if not blocks or (block_name, block_id, t_idx) in blocks or (block_name, block_id, None) in blocks:
+                m.set_model_attn2_replace(pladis_attention, block_name, block_id, t_idx)
+
+        return (m,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "PLADIS": Pladis,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PLADIS": "PLADIS",
+}