54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
from typing import *
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from transformers import AutoModelForImageSegmentation
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import torch
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from torchvision import transforms
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from PIL import Image
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from ...model_revisions import RMBG_REPO, RMBG_REVISION
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class BiRefNet:
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def __init__(
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self,
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model_name: str = RMBG_REPO,
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revision: Optional[str] = RMBG_REVISION,
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cache_dir: Optional[str] = None,
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local_files_only: bool = False,
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):
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self.model = AutoModelForImageSegmentation.from_pretrained(
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model_name,
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revision=revision,
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cache_dir=cache_dir,
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local_files_only=local_files_only,
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trust_remote_code=True,
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)
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self.model.eval()
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self.transform_image = transforms.Compose(
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[
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transforms.Resize((1024, 1024)),
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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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)
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self._device = 'cpu'
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def to(self, device):
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self._device = device
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self.model.to(device)
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def cuda(self):
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self.to('cuda')
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def cpu(self):
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self.to('cpu')
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def __call__(self, image: Image.Image) -> Image.Image:
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image_size = image.size
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input_images = self.transform_image(image).unsqueeze(0).to(self._device)
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with torch.no_grad():
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preds = self.model(input_images)[-1].sigmoid().cpu()
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pred = preds[0].squeeze()
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pred_pil = transforms.ToPILImage()(pred)
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mask = pred_pil.resize(image_size)
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image.putalpha(mask)
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return image
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