61 lines
1.7 KiB
Markdown
61 lines
1.7 KiB
Markdown
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# Documentation
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- Class name: TEEDPreprocessor
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- Category: ControlNet Preprocessors/Line Extractors
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- Output node: False
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TEEDPreprocessor节点旨在使用TEED (TEDDetector)模型对图像进行预处理，以提取软边缘线条。它通过应用专门的线条提取技术来增强输入图像，使其适用于进一步的处理或分析。
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# Input types
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## Required
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- image
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- 需要进行软边缘线条提取处理的输入图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- safe_steps
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- 定义线条提取过程中要采取的安全步骤数，影响检测的鲁棒性和敏感性。
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- Comfy dtype: INT
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- Python dtype: int
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- resolution
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- 指定图像应该被处理的分辨率，影响提取线条的细节水平。
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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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- 输出是一个经过线条提取处理的图像，突出显示了软边缘，使其更适合需要详细线条信息的任务。
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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 TEED_Preprocessor:
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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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safe_steps=("INT", {"default": 2, "min": 0, "max": 10})
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, safe_steps=2, resolution=512, **kwargs):
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from controlnet_aux.teed import TEDDetector
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model = TEDDetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, safe_steps=safe_steps)
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del model
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return (out, )
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```
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