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| import os | |
| import cv2 | |
| import torch | |
| import modules.face_restoration | |
| import modules.shared | |
| from modules import shared, devices, modelloader, errors | |
| from modules.paths import models_path | |
| # codeformer people made a choice to include modified basicsr library to their project which makes | |
| # it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN. | |
| # I am making a choice to include some files from codeformer to work around this issue. | |
| model_dir = "Codeformer" | |
| model_path = os.path.join(models_path, model_dir) | |
| model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' | |
| codeformer = None | |
| def setup_model(dirname): | |
| os.makedirs(model_path, exist_ok=True) | |
| path = modules.paths.paths.get("CodeFormer", None) | |
| if path is None: | |
| return | |
| try: | |
| from torchvision.transforms.functional import normalize | |
| from modules.codeformer.codeformer_arch import CodeFormer | |
| from basicsr.utils import img2tensor, tensor2img | |
| from facelib.utils.face_restoration_helper import FaceRestoreHelper | |
| from facelib.detection.retinaface import retinaface | |
| net_class = CodeFormer | |
| class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration): | |
| def name(self): | |
| return "CodeFormer" | |
| def __init__(self, dirname): | |
| self.net = None | |
| self.face_helper = None | |
| self.cmd_dir = dirname | |
| def create_models(self): | |
| if self.net is not None and self.face_helper is not None: | |
| self.net.to(devices.device_codeformer) | |
| return self.net, self.face_helper | |
| model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth']) | |
| if len(model_paths) != 0: | |
| ckpt_path = model_paths[0] | |
| else: | |
| print("Unable to load codeformer model.") | |
| return None, None | |
| net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer) | |
| checkpoint = torch.load(ckpt_path)['params_ema'] | |
| net.load_state_dict(checkpoint) | |
| net.eval() | |
| if hasattr(retinaface, 'device'): | |
| retinaface.device = devices.device_codeformer | |
| face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer) | |
| self.net = net | |
| self.face_helper = face_helper | |
| return net, face_helper | |
| def send_model_to(self, device): | |
| self.net.to(device) | |
| self.face_helper.face_det.to(device) | |
| self.face_helper.face_parse.to(device) | |
| def restore(self, np_image, w=None): | |
| np_image = np_image[:, :, ::-1] | |
| original_resolution = np_image.shape[0:2] | |
| self.create_models() | |
| if self.net is None or self.face_helper is None: | |
| return np_image | |
| self.send_model_to(devices.device_codeformer) | |
| self.face_helper.clean_all() | |
| self.face_helper.read_image(np_image) | |
| self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) | |
| self.face_helper.align_warp_face() | |
| for cropped_face in self.face_helper.cropped_faces: | |
| 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(devices.device_codeformer) | |
| try: | |
| with torch.no_grad(): | |
| output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] | |
| restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) | |
| del output | |
| devices.torch_gc() | |
| except Exception: | |
| errors.report('Failed inference for CodeFormer', exc_info=True) | |
| restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) | |
| restored_face = restored_face.astype('uint8') | |
| self.face_helper.add_restored_face(restored_face) | |
| self.face_helper.get_inverse_affine(None) | |
| restored_img = self.face_helper.paste_faces_to_input_image() | |
| restored_img = restored_img[:, :, ::-1] | |
| if original_resolution != restored_img.shape[0:2]: | |
| restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) | |
| self.face_helper.clean_all() | |
| if shared.opts.face_restoration_unload: | |
| self.send_model_to(devices.cpu) | |
| return restored_img | |
| global codeformer | |
| codeformer = FaceRestorerCodeFormer(dirname) | |
| shared.face_restorers.append(codeformer) | |
| except Exception: | |
| errors.report("Error setting up CodeFormer", exc_info=True) | |
| # sys.path = stored_sys_path | |