149 lines
5.4 KiB
Python
149 lines
5.4 KiB
Python
import torch
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from .imagefunc import *
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import torch.nn as nn
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from torchvision import transforms
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from .BiRefNet.baseline import BiRefNet
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# from .BiRefNet import config
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from .BiRefNet.config import Config
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NODE_NAME = 'BiRefNetUltra'
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config = Config()
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class BiRefNet_img_processor:
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def __init__(self, config):
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self.config = config
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self.data_size = (config.size, config.size)
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self.transform_image = transforms.Compose([
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transforms.Resize(self.data_size),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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def __call__(self, _image: np.array):
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_image_rs = cv2.resize(_image, (self.config.size, self.config.size), interpolation=cv2.INTER_LINEAR)
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_image_rs = Image.fromarray(np.uint8(_image_rs*255)).convert('RGB')
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image = self.transform_image(_image_rs)
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return image
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class BiRefNetUltra:
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def __init__(self):
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self.ready = False
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def load(self, weight_path, device):
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# load model
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self.model = BiRefNet()
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state_dict = torch.load(weight_path, map_location='cpu')
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unwanted_prefix = '_orig_mod.'
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for k, v in list(state_dict.items()):
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if k.startswith(unwanted_prefix):
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state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
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self.model.load_state_dict(state_dict)
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self.model = self.model.to(device)
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self.model.eval()
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# load processor
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self.processor = BiRefNet_img_processor(config)
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self.ready = True
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@classmethod
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def INPUT_TYPES(cls):
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method_list = ['VITMatte', '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}),
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"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01}),
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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 torch.backends.mps.is_available():
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device = "mps"
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elif torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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if not self.ready:
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model_folder_name = 'BiRefNet'
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model_name = 'BiRefNet-ep480.pth'
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model_file_path = ""
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try:
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model_file_path = os.path.join(
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os.path.normpath(folder_paths.folder_names_and_paths[model_folder_name][0][0]), model_name)
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except:
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pass
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if not os.path.exists(model_file_path):
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model_file_path = os.path.join(folder_paths.models_dir, model_folder_name, model_name)
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self.load(model_file_path, device=device)
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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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np_image = i.squeeze().numpy()
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img = self.processor(np_image)
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inputs = img[None, ...].to(device)
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with torch.no_grad():
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scaled_preds = self.model(inputs)[-1].sigmoid()
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_mask = nn.functional.interpolate(
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scaled_preds[0].unsqueeze(0),
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size=np_image.shape[:2],
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mode='bilinear',
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align_corners=True
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)[0]
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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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brightness_image = ImageEnhance.Brightness(tensor2pil(_mask))
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_mask = brightness_image.enhance(factor=1.01)
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_mask = pil2tensor(_mask)
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_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
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_mask = generate_VITMatte(orig_image, _trimap)
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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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# return (None, 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",
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}
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