[Inference.Core] Fake Scribble Lines (aka scribble_hed)

Documentation

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

The Inference_Core_FakeScribblePreprocessor node is designed for preprocessing images to generate fake scribble lines, simulating the appearance of hand-drawn scribbles. It utilizes a modified HED edge detection model to create stylized line drawings that mimic the scribble effect, offering an alternative to traditional edge detection methods for artistic and creative applications.

Input types

Required

• image
  • The input image to be processed for fake scribble line generation. It serves as the primary data for the node's execution, determining the visual output.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• safe
  • A mode selector that enables or disables safety features during the image processing, affecting the final appearance of the scribble lines.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• resolution
  • Specifies the resolution at which the image processing should be performed, impacting the detail level of the generated scribble lines.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The processed image with fake scribble lines, representing a stylized version of the original input.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class Fake_Scribble_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, scribble=True, safe=kwargs["safe"]=="enable")
        del model
        return (out, )