62 lines
2.0 KiB
Markdown
62 lines
2.0 KiB
Markdown
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# Documentation
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- Class name: Scribble_XDoG_Preprocessor
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- Category: ControlNet Preprocessors/Line Extractors
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- Output node: False
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Scribble_XDoG_Preprocessor节点专门用于从图像中提取风格化的线条绘图，结合了涂鸦检测和XDoG（扩展高斯差分）过滤技术。它对图像进行预处理以突出重要的边缘和细节，使其适合进一步的艺术或分析处理。
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# Input types
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## Required
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- image
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- 需要处理以提取线条的输入图像。它是节点应用涂鸦检测和XDoG过滤技术的主要数据。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- threshold
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- 决定XDoG过滤器在检测边缘时的敏感度。较低的阈值会捕捉到更细微的细节，而较高的值则强调更显著的边缘。
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- Comfy dtype: INT
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- Python dtype: int
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- resolution
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- 指定处理图像的分辨率。它影响提取的线条中捕获的细节水平。
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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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- 输出是一个经过处理的图像，包含风格化的线条绘图，强调了使用Scribble_XDoG技术提取的边缘和细节。
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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: `CPU`
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- Common nodes:
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- [PreviewImage](../../Comfy/Nodes/PreviewImage.md)
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- [CR Multi-ControlNet Stack](../../ComfyUI_Comfyroll_CustomNodes/Nodes/CR Multi-ControlNet Stack.md)
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## Source code
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```python
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class Scribble_XDoG_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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threshold = ("INT", {"default": 32, "min": 1, "max": 64, "step": 1})
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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, threshold, resolution=512, **kwargs):
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from controlnet_aux.scribble import ScribbleXDog_Detector
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model = ScribbleXDog_Detector()
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return (common_annotator_call(model, image, resolution=resolution, thr_a=threshold), )
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```
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