| import os |
|
|
| import facexlib |
| import gfpgan |
|
|
| import modules.face_restoration |
| from modules import paths, shared, devices, modelloader, errors |
|
|
| 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 models[0].startswith("http"): |
| 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): |
| try: |
| os.makedirs(model_path, exist_ok=True) |
| 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: |
| errors.report("Error setting up GFPGAN", exc_info=True) |
|
|
