CavinHuang-comfyui-nodes-docs/docs/Inference_Core_UniFormer-SemSegPreprocessor.md

Documentation

• Class name: Inference_Core_UniFormer-SemSegPreprocessor
• Category: ControlNet Preprocessors/Semantic Segmentation
• Output node: False

该节点是为语义分割任务设计的，它利用UniFormer模型来处理图像并生成语义分割图。该节点将模型加载、图像预处理和推理等复杂操作抽象为一个流畅的过程，从而实现高效准确的图像分割。

Input types

Required

• image
  • 需要进行语义分割的输入图像。这个参数对于确定输出分割图的内容和结构至关重要。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• resolution
  • 指定在处理前将输入图像调整到的分辨率。这会影响分割输出的精细度和质量。
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • 输入图像的语义分割图，以张量形式表示。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class Uniformer_SemSegPreprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return create_node_input_types()

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

    CATEGORY = "ControlNet Preprocessors/Semantic Segmentation"

    def semantic_segmentate(self, image, resolution=512):
        from controlnet_aux.uniformer import UniformerSegmentor

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