54 lines
1.6 KiB
Markdown
54 lines
1.6 KiB
Markdown
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# Documentation
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- Class name: UniFormer-SemSegPreprocessor
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- Category: ControlNet Preprocessors/Semantic Segmentation
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- Output node: False
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UniFormer-SemSegPreprocessor节点是一个专门用于语义分割任务的处理器，属于ControlNet预处理器中的语义分割类别。它利用UniformerSegmentor模型来处理图像，旨在理解并将图像分割成不同的语义区域，这些区域基于学习到的表示。
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# Input types
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## Required
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- image
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- 需要进行语义分割的输入图像。这个参数至关重要，因为它是模型执行分割操作的主要数据源。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- resolution
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- 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细节水平。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 语义分割过程的输出，是一个被分割成不同语义区域的图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class Uniformer_SemSegPreprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return create_node_input_types()
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "semantic_segmentate"
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CATEGORY = "ControlNet Preprocessors/Semantic Segmentation"
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def semantic_segmentate(self, image, resolution=512):
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from controlnet_aux.uniformer import UniformerSegmentor
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model = UniformerSegmentor.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution)
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del model
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return (out, )
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```
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