node_wrappers/hed.py

from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
import comfy.model_management as model_management

class HED_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return define_preprocessor_inputs(
            safe=INPUT.COMBO(["enable", "disable"]),
            resolution=INPUT.RESOLUTION()
        )

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    CATEGORY = "ControlNet Preprocessors/Line Extractors"

    def execute(self, image, resolution=512, **kwargs):
        from custom_controlnet_aux.hed import HEDdetector

        model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
        out = common_annotator_call(model, image, resolution=resolution, safe = kwargs["safe"] == "enable")
        del model
        return (out, )

class Fake_Scribble_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return define_preprocessor_inputs(
            safe=INPUT.COMBO(["enable", "disable"]),
            resolution=INPUT.RESOLUTION()
        )

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    CATEGORY = "ControlNet Preprocessors/Line Extractors"

    def execute(self, image, resolution=512, **kwargs):
        from custom_controlnet_aux.hed import HEDdetector
        
        model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
        out = common_annotator_call(model, image, resolution=resolution, scribble=True, safe=kwargs["safe"]=="enable")
        del model
        return (out, )

NODE_CLASS_MAPPINGS = {
    "HEDPreprocessor": HED_Preprocessor,
    "FakeScribblePreprocessor": Fake_Scribble_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
    "HEDPreprocessor": "HED Soft-Edge Lines",
    "FakeScribblePreprocessor": "Fake Scribble Lines (aka scribble_hed)"
}