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| import os | |
| import sys | |
| import traceback | |
| import facexlib | |
| import gfpgan | |
| import modules.face_restoration | |
| from modules import paths, shared, devices, modelloader | |
| model_dir = "GFPGAN" | |
| user_path = None | |
| model_path = os.path.join(paths.models_path, model_dir) | |
| model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" | |
| have_gfpgan = False | |
| loaded_gfpgan_model = None | |
| def gfpgann(): | |
| global loaded_gfpgan_model | |
| global model_path | |
| if loaded_gfpgan_model is not None: | |
| loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan) | |
| return loaded_gfpgan_model | |
| if gfpgan_constructor is None: | |
| return None | |
| models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN") | |
| if len(models) == 1 and "http" in models[0]: | |
| model_file = models[0] | |
| elif len(models) != 0: | |
| latest_file = max(models, key=os.path.getctime) | |
| model_file = latest_file | |
| else: | |
| print("Unable to load gfpgan model!") | |
| return None | |
| if hasattr(facexlib.detection.retinaface, 'device'): | |
| facexlib.detection.retinaface.device = devices.device_gfpgan | |
| model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan) | |
| loaded_gfpgan_model = model | |
| return model | |
| def send_model_to(model, device): | |
| model.gfpgan.to(device) | |
| model.face_helper.face_det.to(device) | |
| model.face_helper.face_parse.to(device) | |
| def gfpgan_fix_faces(np_image): | |
| model = gfpgann() | |
| if model is None: | |
| return np_image | |
| send_model_to(model, devices.device_gfpgan) | |
| np_image_bgr = np_image[:, :, ::-1] | |
| cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) | |
| np_image = gfpgan_output_bgr[:, :, ::-1] | |
| model.face_helper.clean_all() | |
| if shared.opts.face_restoration_unload: | |
| send_model_to(model, devices.cpu) | |
| return np_image | |
| gfpgan_constructor = None | |
| def setup_model(dirname): | |
| global model_path | |
| if not os.path.exists(model_path): | |
| os.makedirs(model_path) | |
| try: | |
| from gfpgan import GFPGANer | |
| from facexlib import detection, parsing | |
| global user_path | |
| global have_gfpgan | |
| global gfpgan_constructor | |
| load_file_from_url_orig = gfpgan.utils.load_file_from_url | |
| facex_load_file_from_url_orig = facexlib.detection.load_file_from_url | |
| facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url | |
| def my_load_file_from_url(**kwargs): | |
| return load_file_from_url_orig(**dict(kwargs, model_dir=model_path)) | |
| def facex_load_file_from_url(**kwargs): | |
| return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None)) | |
| def facex_load_file_from_url2(**kwargs): | |
| return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None)) | |
| gfpgan.utils.load_file_from_url = my_load_file_from_url | |
| facexlib.detection.load_file_from_url = facex_load_file_from_url | |
| facexlib.parsing.load_file_from_url = facex_load_file_from_url2 | |
| user_path = dirname | |
| have_gfpgan = True | |
| gfpgan_constructor = GFPGANer | |
| class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration): | |
| def name(self): | |
| return "GFPGAN" | |
| def restore(self, np_image): | |
| return gfpgan_fix_faces(np_image) | |
| shared.face_restorers.append(FaceRestorerGFPGAN()) | |
| except Exception: | |
| print("Error setting up GFPGAN:", file=sys.stderr) | |
| print(traceback.format_exc(), file=sys.stderr) | |