import os import sys import traceback import cv2 import torch from modules import shared from modules.paths import script_path import modules.shared import modules.face_restoration from importlib import reload # codeformer people made a choice to include modified basicsr librry to their projectwhich makes # it utterly impossiblr 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. pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' have_codeformer = False codeformer = None def setup_codeformer(): path = modules.paths.paths.get("CodeFormer", None) if path is None: return # both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own #stored_sys_path = sys.path #sys.path = [path] + sys.path try: from torchvision.transforms.functional import normalize from modules.codeformer.codeformer_arch import CodeFormer from basicsr.utils.download_util import load_file_from_url from basicsr.utils import imwrite, img2tensor, tensor2img from facelib.utils.face_restoration_helper import FaceRestoreHelper from modules.shared import cmd_opts net_class = CodeFormer class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration): def name(self): return "CodeFormer" def __init__(self): self.net = None self.face_helper = None def create_models(self): if self.net is not None and self.face_helper is not None: return self.net, self.face_helper net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device) ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True) checkpoint = torch.load(ckpt_path)['params_ema'] net.load_state_dict(checkpoint) net.eval() face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device) if not cmd_opts.unload_gfpgan: self.net = net self.face_helper = face_helper return net, face_helper def restore(self, np_image, w=None): np_image = np_image[:, :, ::-1] original_resolution = np_image.shape[0:2] net, face_helper = self.create_models() face_helper.clean_all() face_helper.read_image(np_image) face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) face_helper.align_warp_face() for idx, cropped_face in enumerate(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(shared.device) try: with torch.no_grad(): output = 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 torch.cuda.empty_cache() except Exception as error: print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr) 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) face_helper.get_inverse_affine(None) restored_img = 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) return restored_img global have_codeformer have_codeformer = True global codeformer codeformer = FaceRestorerCodeFormer() shared.face_restorers.append(codeformer) except Exception: print("Error setting up CodeFormer:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) # sys.path = stored_sys_path