63 lines
2.0 KiB
Markdown
63 lines
2.0 KiB
Markdown
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# Documentation
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- Class name: FakeScribblePreprocessor
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- Category: ControlNet Preprocessors/Line Extractors
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- Output node: False
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FakeScribblePreprocessor节点用于预处理图像以模拟手绘线条效果，它利用了经过修改的HED（全息嵌套边缘检测）模型。该节点旨在生成具有类似手绘线条的图像，这在各种图像处理和计算机视觉任务中非常有用，特别是在需要将边缘风格化为手绘线条的场景中。
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# Input types
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## Required
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- image
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- 需要处理以模拟手绘线条的输入图像。
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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
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- 一种模式，启用时会应用安全机制到预处理过程中，可能会改变处理方式以避免不良效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- resolution
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- 执行图像处理的分辨率。此参数允许调整输出图像的细节级别。
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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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- 从输入图像处理得到的模拟手绘线条的输出图像。
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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:
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- [ACN_AdvancedControlNetApply](../../ComfyUI-Advanced-ControlNet/Nodes/ACN_AdvancedControlNetApply.md)
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- [PreviewImage](../../Comfy/Nodes/PreviewImage.md)
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## Source code
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```python
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class Fake_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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safe=(["enable", "disable"], {"default": "enable"})
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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, resolution=512, **kwargs):
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from controlnet_aux.hed import HEDdetector
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model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, scribble=True, safe=kwargs["safe"]=="enable")
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del model
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return (out, )
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```
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