HED Soft-Edge Lines

Documentation

• Class name: HEDPreprocessor
• Category: ControlNet Preprocessors/Line Extractors
• Output node: False

The HEDPreprocessor node is designed for extracting soft-edge lines from images using the HED (Holistically-Nested Edge Detection) model. It preprocesses images to enhance or isolate their line features, making it suitable for tasks that require detailed edge or line detection.

Input types

Required

• image
  • The input image to be processed for edge detection.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• safe
  • A toggle to enable or disable safety checks during processing, potentially affecting the output's fidelity.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• resolution
  • The resolution to which the input image is resized before processing. Higher resolutions may improve detail at the cost of increased computation.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The processed image with enhanced or isolated soft-edge lines.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes:
  • ControlNetApplyAdvanced
  • PreviewImage
  • ACN_AdvancedControlNetApply

Source code

class HED_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return create_node_input_types(
            safe=(["enable", "disable"], {"default": "enable"})
        )

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

    CATEGORY = "ControlNet Preprocessors/Line Extractors"

    def execute(self, image, resolution=512, **kwargs):
        from 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, )