132 lines
4.6 KiB
Markdown
132 lines
4.6 KiB
Markdown
# Documentation
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- Class name: BiRefNetUltra
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- Category: 😺dzNodes/LayerMask
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- Output node: False
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- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
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使用BiRefNet模型去除背景，有更好的识别能力，同时具有超高的边缘细节。
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本节点模型部分的代码来自vipery的[ComfyUI-BiRefNet](https://github.com/viperyl/ComfyUI-BiRefNet)，感谢原作者。
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# Input types
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## Required
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- image
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- 输入的图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- detail_method
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- 边缘处理方法。提供了VITMatte, VITMatte(local), PyMatting, GuidedFilter。如果首次使用VITMatte后模型已经下载，之后可以使用VITMatte(local)。
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- Comfy dtype: STRING
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- Python dtype: str
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- Choices: ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter']
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- detail_erode
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- 遮罩边缘向内侵蚀范围。数值越大，向内修复的范围越大
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- Comfy dtype: INT
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- Python dtype: int
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- detail_dilate
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- 遮罩边缘向外膨胀范围。数值越大，向外修复的范围越大
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- Comfy dtype: INT
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- Python dtype: int
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- black_point
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- 遮罩黑色点的阈值。数值越小，黑色点越多
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- Comfy dtype: FLOAT
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- Python dtype: float
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- white_point
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- 遮罩白色点的阈值。数值越大，白色点越多
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- Comfy dtype: FLOAT
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- Python dtype: float
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- process_detail
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- 是否处理遮罩的细节。如果选择否，将直接输出原始遮罩, 此处设为False将跳过边缘处理以节省运行时间。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- image
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- 输出的图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 输出的遮罩。
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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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# Source code
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```
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class BiRefNetUltra:
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@classmethod
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def INPUT_TYPES(cls):
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method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ]
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return {
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"required": {
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"image": ("IMAGE",),
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"detail_method": (method_list,),
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"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
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"detail_dilate": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
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"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
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"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
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"process_detail": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", )
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RETURN_NAMES = ("image", "mask", )
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FUNCTION = "birefnet_ultra"
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CATEGORY = '😺dzNodes/LayerMask'
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def birefnet_ultra(self, image, detail_method, detail_erode, detail_dilate,
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black_point, white_point, process_detail):
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ret_images = []
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ret_masks = []
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if detail_method == 'VITMatte(local)':
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local_files_only = True
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else:
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local_files_only = False
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from .birefnet_func import BiRefNetRemoveBackground
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birefnetrmbg = BiRefNetRemoveBackground()
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for i in image:
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i = torch.unsqueeze(i, 0)
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orig_image = tensor2pil(i).convert('RGB')
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_mask = birefnetrmbg.generate_mask(orig_image)
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_mask = image2mask(_mask)
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detail_range = detail_erode + detail_dilate
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if process_detail:
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if detail_method == 'GuidedFilter':
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_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
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_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
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elif detail_method == 'PyMatting':
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_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
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else:
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_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
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_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only)
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_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
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else:
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_mask = tensor2pil(_mask)
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ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
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ret_images.append(pil2tensor(ret_image))
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ret_masks.append(image2mask(_mask))
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log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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``` |