84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
# layerstyle advance
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from .imagefunc import *
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NODE_NAME = 'BiRefNetUltra'
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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)', 'vitmatte-base-composition-1k', 'PyMatting', 'GuidedFilter', ]
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device_list = ['cuda','cpu']
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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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"device": (device_list,),
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"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}),
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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, device, max_megapixels):
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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_legacy 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, device=device, max_megapixels=max_megapixels, method=detail_method)
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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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NODE_CLASS_MAPPINGS = {
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"LayerMask: BiRefNetUltra": BiRefNetUltra,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: BiRefNetUltra": "LayerMask: BiRefNetUltra(Advance)",
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}
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