55 lines
1.6 KiB
Markdown
55 lines
1.6 KiB
Markdown
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# Documentation
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- Class name: ScribblePreprocessor
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- Category: ControlNet Preprocessors/Line Extractors
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- Output node: False
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ScribblePreprocessor节点旨在预处理图像以检测涂鸦线条。它利用特定模型来分析和处理输入图像，目的是突出或提取类似涂鸦的图案。
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# Input types
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## Required
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- image
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- 需要处理以进行涂鸦线条检测的输入图像。这个参数至关重要，因为它提供了涂鸦检测模型操作的视觉数据。
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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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- 指定在处理之前将输入图像调整到的分辨率。这会影响涂鸦线条检测的细节程度。
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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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- 经处理后检测或突出显示涂鸦线条的图像。这个输出对于涂鸦图案的进一步处理或可视化很有用。
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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:
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- [PreviewImage](../../Comfy/Nodes/PreviewImage.md)
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- [ControlNetApplyAdvanced](../../Comfy/Nodes/ControlNetApplyAdvanced.md)
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## Source code
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```python
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class Scribble_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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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, resolution=512, **kwargs):
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from controlnet_aux.scribble import ScribbleDetector
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model = ScribbleDetector()
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return (common_annotator_call(model, image, resolution=resolution), )
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```
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