65 lines
1.9 KiB
Markdown
65 lines
1.9 KiB
Markdown
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# Documentation
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- Class name: HEDPreprocessor
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- Category: ControlNet Preprocessors/Line Extractors
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- Output node: False
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HEDPreprocessor节点使用HED（全息嵌套边缘检测）模型从图像中提取软边缘线条。它预处理图像以增强或分离线条特征，适用于需要详细边缘或线条检测的任务。
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# Input types
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## Required
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- image
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- 需要进行边缘检测处理的输入图像。
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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
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- 一个开关，用于启用或禁用处理过程中的安全检查，可能会影响输出的保真度。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- resolution
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- 在处理前将输入图像调整到的分辨率。更高的分辨率可能会提高细节，但会增加计算量。
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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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- 经过处理后的图像，具有增强或分离的软边缘线条。
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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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- [PreviewImage](../../Comfy/Nodes/PreviewImage.md)
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- [ACN_AdvancedControlNetApply](../../ComfyUI-Advanced-ControlNet/Nodes/ACN_AdvancedControlNetApply.md)
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## Source code
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```python
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class HED_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, safe = kwargs["safe"] == "enable")
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del model
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return (out, )
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```
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