Add custom nodes, Civitai loras (LFS), and vast.ai setup script
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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>
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53
custom_nodes/comfyui_controlnet_aux/node_wrappers/hed.py
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53
custom_nodes/comfyui_controlnet_aux/node_wrappers/hed.py
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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
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import comfy.model_management as model_management
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class HED_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return define_preprocessor_inputs(
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safe=INPUT.COMBO(["enable", "disable"]),
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resolution=INPUT.RESOLUTION()
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, resolution=512, **kwargs):
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from custom_controlnet_aux.hed import HEDdetector
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model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, safe = kwargs["safe"] == "enable")
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del model
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return (out, )
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class Fake_Scribble_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return define_preprocessor_inputs(
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safe=INPUT.COMBO(["enable", "disable"]),
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resolution=INPUT.RESOLUTION()
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, resolution=512, **kwargs):
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from custom_controlnet_aux.hed import HEDdetector
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model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, scribble=True, safe=kwargs["safe"]=="enable")
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del model
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return (out, )
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NODE_CLASS_MAPPINGS = {
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"HEDPreprocessor": HED_Preprocessor,
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"FakeScribblePreprocessor": Fake_Scribble_Preprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"HEDPreprocessor": "HED Soft-Edge Lines",
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"FakeScribblePreprocessor": "Fake Scribble Lines (aka scribble_hed)"
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}
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