Merge pull request #13718 from avantcontra/bugfix_gfpgan_custom_path

model_dir = "GFPGAN"

user_path = None

model_path = os.path.join(paths.models_path, model_dir)

model_file_path = None

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

    global model_file_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")

    models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])

    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)

        gfp_models = []

        for item in models:

            if 'GFPGAN' in os.path.basename(item):

                gfp_models.append(item)

        latest_file = max(gfp_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_file_path = model_file

    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

        global user_path

        global have_gfpgan

        global gfpgan_constructor

        global model_file_path

        facexlib_path = model_path

        if dirname is not None:

            facexlib_path = dirname

        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))

            return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path))

        def facex_load_file_from_url(**kwargs):

            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))

            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_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))

            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_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