CavinHuang-comfyui-nodes-docs/docs/LayerMask%3A BiRefNetUltra.md

Documentation

• Class name: BiRefNetUltra
• Category: 😺dzNodes/LayerMask
• Output node: False
• Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle

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。 本节点模型部分的代码来自vipery的ComfyUI-BiRefNet，感谢原作者。

Input types

Required

• image

  • 输入的图片。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

• detail_method

  • 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)。
  • Comfy dtype: STRING
  • Python dtype: str
  • Choices: ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter']

• detail_erode

  • 遮罩边缘向内侵蚀范围。数值越大，向内修复的范围越大
  • Comfy dtype: INT
  • Python dtype: int

• detail_dilate

  • 遮罩边缘向外膨胀范围。数值越大，向外修复的范围越大
  • Comfy dtype: INT
  • Python dtype: int

• black_point

  • 遮罩黑色点的阈值。数值越小，黑色点越多
  • Comfy dtype: FLOAT
  • Python dtype: float

• white_point

  • 遮罩白色点的阈值。数值越大，白色点越多
  • Comfy dtype: FLOAT
  • Python dtype: float

• process_detail

  • 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。
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• image

  • 输出的图片。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

• mask

  • 输出的遮罩。
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU

Source code

class BiRefNetUltra:

    @classmethod
    def INPUT_TYPES(cls):

        method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ]

        return {
            "required": {
                "image": ("IMAGE",),
                "detail_method": (method_list,),
                "detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
                "detail_dilate": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
                "black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
                "white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
                "process_detail": ("BOOLEAN", {"default": True}),
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE", "MASK", )
    RETURN_NAMES = ("image", "mask", )
    FUNCTION = "birefnet_ultra"
    CATEGORY = '😺dzNodes/LayerMask'

    def birefnet_ultra(self, image, detail_method, detail_erode, detail_dilate,
                       black_point, white_point, process_detail):
        ret_images = []
        ret_masks = []

        if detail_method == 'VITMatte(local)':
            local_files_only = True
        else:
            local_files_only = False

        from .birefnet_func import BiRefNetRemoveBackground
        birefnetrmbg = BiRefNetRemoveBackground()

        for i in image:
            i = torch.unsqueeze(i, 0)
            orig_image = tensor2pil(i).convert('RGB')

            _mask = birefnetrmbg.generate_mask(orig_image)
            _mask = image2mask(_mask)

            detail_range = detail_erode + detail_dilate

            if process_detail:
                if detail_method == 'GuidedFilter':
                    _mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
                    _mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
                elif detail_method == 'PyMatting':
                    _mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
                else:
                    _trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
                    _mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only)
                    _mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
            else:
                _mask = tensor2pil(_mask)

            ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
            ret_images.append(pil2tensor(ret_image))
            ret_masks.append(image2mask(_mask))

        log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)