Spaces:
Running
Running
| import torch | |
| from torchvision.transforms.functional import normalize | |
| from models import CodeFormer | |
| from utils import imwrite, img2tensor, tensor2img | |
| from facelib.utils.face_restoration_helper import FaceRestoreHelper | |
| from huggingface_hub import hf_hub_download | |
| REPO_ID = "leonelhs/gfpgan" | |
| pretrain_model_path = hf_hub_download(repo_id=REPO_ID, filename="CodeFormer.pth") | |
| if __name__ == '__main__': | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| net = CodeFormer(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, | |
| connect_list=['32', '64', '128', '256']).to(device) | |
| checkpoint = torch.load(pretrain_model_path)['params_ema'] | |
| net.load_state_dict(checkpoint) | |
| net.eval() | |
| face_helper = FaceRestoreHelper( | |
| upscale_factor=2, | |
| face_size=512, | |
| crop_ratio=(1, 1), | |
| det_model='retinaface_resnet50', | |
| save_ext='png', | |
| use_parse=True, | |
| device=device) | |
| input_img_list = ["/home/leonel/Pictures/lowres13.jpg"] | |
| # -------------------- start to processing --------------------- | |
| for i, img_path in enumerate(input_img_list): | |
| # clean all the intermediate results to process the next image | |
| face_helper.clean_all() | |
| img = img_path | |
| face_helper.read_image(img) | |
| # get face landmarks for each face | |
| num_det_faces = face_helper.get_face_landmarks_5( | |
| only_center_face=False, resize=640, eye_dist_threshold=5) | |
| print(f'\tdetect {num_det_faces} faces') | |
| # align and warp each face | |
| face_helper.align_warp_face() | |
| # face restoration for each cropped face | |
| for idx, cropped_face in enumerate(face_helper.cropped_faces): | |
| # prepare data | |
| cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) | |
| normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) | |
| cropped_face_t = cropped_face_t.unsqueeze(0).to(device) | |
| try: | |
| with torch.no_grad(): | |
| output = net(cropped_face_t, w=0.5, adain=True)[0] | |
| restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) | |
| del output | |
| torch.cuda.empty_cache() | |
| except Exception as error: | |
| print(f'\tFailed inference for CodeFormer: {error}') | |
| restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) | |
| restored_face = restored_face.astype('uint8') | |
| face_helper.add_restored_face(restored_face, cropped_face) | |
| # paste_back | |
| has_aligned = False | |
| suffix = None | |
| if not has_aligned: | |
| bg_img = None | |
| face_helper.get_inverse_affine(None) | |
| restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=False) | |
| imwrite(restored_img, "pretty.png") | |