Increased GPU usage
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@@ -9,8 +9,15 @@ from basicsr.utils.download_util import load_file_from_url
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from facelib.utils.face_restoration_helper import FaceRestoreHelper
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from basicsr.utils.registry import ARCH_REGISTRY
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# Cross-platform device selection: CUDA > MPS > CPU
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if torch.cuda.is_available():
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device = torch.device("cuda")
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elif torch.backends.mps.is_available():
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device = torch.device("mps")
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else:
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device = torch.device("cpu")
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# Download and load model
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pretrain_model_url = {
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'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
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}
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@@ -20,7 +27,7 @@ net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8
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ckpt_path = load_file_from_url(url=pretrain_model_url['restoration'],
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model_dir='weights/CodeFormer', progress=True, file_name=None)
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checkpoint = torch.load(ckpt_path)['params_ema']
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checkpoint = torch.load(ckpt_path, map_location=device)['params_ema']
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net.load_state_dict(checkpoint)
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net.eval()
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@@ -47,9 +54,9 @@ def _enhance_img(img: np.ndarray, w: float = 0.5) -> np.ndarray:
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face_helper.align_warp_face()
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for cropped_face in face_helper.cropped_faces:
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True).to(device)
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
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cropped_face_t = cropped_face_t.unsqueeze(0) # (1, 3, H, W), already on correct device
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with torch.no_grad():
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output = net(cropped_face_t, w=w, adain=True)[0]
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@@ -84,4 +91,4 @@ def enhance_image_memory(img: np.ndarray, w: float = 0.5) -> np.ndarray:
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"""
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Enhances an input image entirely in memory and returns the enhanced image.
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"""
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return _enhance_img(img, w=w)
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return _enhance_img(img, w=w)
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