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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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} |