Inference_Core_TEEDPreprocessor

Documentation

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

The TEED Preprocessor node is designed for preprocessing images to extract soft-edge lines using the TEDDetector model. It adjusts the image processing based on the provided safety steps and resolution, optimizing for control net applications.

Input types

Required

• image
  • The input image to be processed for soft-edge line extraction.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• safe_steps
  • Specifies the number of safety steps to use during preprocessing, affecting the thoroughness and potentially the quality of the line extraction.
  • Comfy dtype: INT
  • Python dtype: int
• resolution
  • The resolution at which the image should be processed, influencing the detail level of the extracted lines.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output is an image that has been processed to highlight soft-edge lines, suitable for further processing or analysis.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class TEED_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return create_node_input_types(
            safe_steps=("INT", {"default": 2, "min": 0, "max": 10})
        )

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

    CATEGORY = "ControlNet Preprocessors/Line Extractors"

    def execute(self, image, safe_steps=2, resolution=512, **kwargs):
        from controlnet_aux.teed import TEDDetector

        model = TEDDetector.from_pretrained().to(model_management.get_torch_device())
        out = common_annotator_call(model, image, resolution=resolution, safe_steps=safe_steps)
        del model
        return (out, )