[Inference.Core] PiDiNet Soft-Edge Lines

Documentation

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

The PiDiNet Preprocessor node is designed for preprocessing images to extract soft-edge lines, utilizing the PiDiNet model for enhanced line detection. It supports configurable safety modes and resolution settings to adapt to various image processing needs.

Input types

Required

• image
  • The input image to be processed for line extraction. It is the primary data on which the PiDiNet model operates.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• safe
  • A mode that enables or disables safety checks during image processing, affecting the execution path and potentially the output quality.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• resolution
  • The resolution to which the input image is resized before processing, impacting the detail level of the extracted lines.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The processed image with extracted lines, showcasing the capabilities of the PiDiNet model in enhancing line detection.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class PIDINET_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, safe, resolution=512, **kwargs):
        from controlnet_aux.pidi import PidiNetDetector

        model = PidiNetDetector.from_pretrained().to(model_management.get_torch_device())
        out = common_annotator_call(model, image, resolution=resolution, safe = safe == "enable")
        del model
        return (out, )