CavinHuang-comfyui-nodes-docs/docs/Inference_Core_TEEDPreprocessor.md

Documentation

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

TEED Preprocessor节点专门用于预处理图像，以使用TEDDetector模型提取软边缘线条。它根据提供的安全步骤和分辨率调整图像处理过程，为控制网应用进行优化。

Input types

Required

• image
  • 需要处理以提取软边缘线条的输入图像。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• safe_steps
  • 指定预处理过程中使用的安全步骤数量，影响线条提取的全面性和潜在质量。
  • Comfy dtype: INT
  • Python dtype: int
• resolution
  • 指定图像处理的分辨率，影响提取线条的细节水平。
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • 输出是一张经过处理以突出显示软边缘线条的图像，适用于进一步处理或分析。
  • Comfy dtype: IMAGE
  • 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, )