63 lines
2.1 KiB
Markdown
63 lines
2.1 KiB
Markdown
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# Documentation
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- Class name: PiDiNetPreprocessor
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- Category: ControlNet Preprocessors/Line Extractors
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- Output node: False
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PiDiNetPreprocessor节点旨在使用PiDiNet模型对图像进行预处理以提取软边缘线条。它的目标是通过提供精炼的输入来增强图像分析和处理任务，为进一步的处理或分析做准备。
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# Input types
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## Required
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- image
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- 需要处理以提取软边缘线条的输入图像。这是节点操作的主要数据，旨在增强其特征以供后续分析使用。
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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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- 一个模式选择器，用于确定是否在安全模式下执行操作。启用"安全"模式可确保预处理遵守安全约束，可能会影响输出的保真度和细节，以降低处理过程中的风险。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- resolution
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- 指定处理图像的分辨率。较高的分辨率会导致更详细的软边缘线条提取，而较低的分辨率可能会加快处理速度，但会牺牲细节。
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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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- 带有提取的软边缘线条的处理后图像，可用于进一步的分析或处理步骤。
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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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- [ControlNetApplyAdvanced](../../Comfy/Nodes/ControlNetApplyAdvanced.md)
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## Source code
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```python
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class PIDINET_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, safe, resolution=512, **kwargs):
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from controlnet_aux.pidi import PidiNetDetector
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model = PidiNetDetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, safe = safe == "enable")
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del model
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return (out, )
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```
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