82 lines
3.0 KiB
Markdown
82 lines
3.0 KiB
Markdown
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# Documentation
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- Class name: AnimeFace_SemSegPreprocessor
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- Category: ControlNet Preprocessors/Semantic Segmentation
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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AnimeFace_SemSegPreprocessor节点专门用于预处理动漫人脸语义分割任务的图像。它利用专门的模型从背景中分割动漫人脸，根据提供的设置可选择性地移除背景，并为进一步处理准备图像和蒙版。
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# Input types
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## Required
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- image
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- 待处理的输入图像，用于动漫人脸分割。这是分割任务的主要对象。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- remove_background_using_abg
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- 一个标志，用于确定在分割过程中是否应从图像中移除背景。这会影响输出的蒙版和分割后的图像。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- resolution
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- 处理前将输入图像调整到的分辨率。这确保了在不同图像尺寸下分割输出的一致性。
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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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- 分割后的动漫人脸图像，根据输入标志可选择性地移除背景。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- ABG_CHARACTER_MASK (MASK)
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- 指示图像中动漫人脸分割区域的蒙版。用于进一步的图像处理或操作。
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- Comfy dtype: MASK
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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: unknown
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## Source code
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```python
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class AnimeFace_SemSegPreprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",)
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},
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"optional": {
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#This preprocessor is only trained on 512x resolution
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#https://github.com/siyeong0/Anime-Face-Segmentation/blob/main/predict.py#L25
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"remove_background_using_abg": ("BOOLEAN", {"default": True}),
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"resolution": ("INT", {"default": 512, "min": 512, "max": 512, "step": 64})
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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RETURN_NAMES = ("IMAGE", "ABG_CHARACTER_MASK (MASK)")
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Semantic Segmentation"
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def execute(self, image, remove_background_using_abg=True, resolution=512, **kwargs):
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from controlnet_aux.anime_face_segment import AnimeFaceSegmentor
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model = AnimeFaceSegmentor.from_pretrained().to(model_management.get_torch_device())
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if remove_background_using_abg:
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out_image_with_mask = common_annotator_call(model, image, resolution=resolution, remove_background=True)
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out_image = out_image_with_mask[..., :3]
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mask = out_image_with_mask[..., 3:]
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mask = rearrange(mask, "n h w c -> n c h w")
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else:
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out_image = common_annotator_call(model, image, resolution=resolution, remove_background=False)
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N, H, W, C = out_image.shape
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mask = torch.ones(N, C, H, W)
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del model
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return (out_image, mask)
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```
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