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Configuration error
| import glob | |
| import os | |
| import shutil | |
| import importlib | |
| from urllib.parse import urlparse | |
| from modules import shared | |
| from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone | |
| from modules.paths import script_path, models_path | |
| def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list: | |
| """ | |
| A one-and done loader to try finding the desired models in specified directories. | |
| @param download_name: Specify to download from model_url immediately. | |
| @param model_url: If no other models are found, this will be downloaded on upscale. | |
| @param model_path: The location to store/find models in. | |
| @param command_path: A command-line argument to search for models in first. | |
| @param ext_filter: An optional list of filename extensions to filter by | |
| @return: A list of paths containing the desired model(s) | |
| """ | |
| output = [] | |
| if ext_filter is None: | |
| ext_filter = [] | |
| try: | |
| places = [] | |
| if command_path is not None and command_path != model_path: | |
| pretrained_path = os.path.join(command_path, 'experiments/pretrained_models') | |
| if os.path.exists(pretrained_path): | |
| print(f"Appending path: {pretrained_path}") | |
| places.append(pretrained_path) | |
| elif os.path.exists(command_path): | |
| places.append(command_path) | |
| places.append(model_path) | |
| for place in places: | |
| if os.path.exists(place): | |
| for file in glob.iglob(place + '**/**', recursive=True): | |
| full_path = file | |
| if os.path.isdir(full_path): | |
| continue | |
| if os.path.islink(full_path) and not os.path.exists(full_path): | |
| print(f"Skipping broken symlink: {full_path}") | |
| continue | |
| if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]): | |
| continue | |
| if len(ext_filter) != 0: | |
| model_name, extension = os.path.splitext(file) | |
| if extension not in ext_filter: | |
| continue | |
| if file not in output: | |
| output.append(full_path) | |
| if model_url is not None and len(output) == 0: | |
| if download_name is not None: | |
| from basicsr.utils.download_util import load_file_from_url | |
| dl = load_file_from_url(model_url, model_path, True, download_name) | |
| output.append(dl) | |
| else: | |
| output.append(model_url) | |
| except Exception: | |
| pass | |
| return output | |
| def friendly_name(file: str): | |
| if "http" in file: | |
| file = urlparse(file).path | |
| file = os.path.basename(file) | |
| model_name, extension = os.path.splitext(file) | |
| return model_name | |
| def cleanup_models(): | |
| # This code could probably be more efficient if we used a tuple list or something to store the src/destinations | |
| # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler | |
| # somehow auto-register and just do these things... | |
| root_path = script_path | |
| src_path = models_path | |
| dest_path = os.path.join(models_path, "Stable-diffusion") | |
| move_files(src_path, dest_path, ".ckpt") | |
| move_files(src_path, dest_path, ".safetensors") | |
| src_path = os.path.join(root_path, "ESRGAN") | |
| dest_path = os.path.join(models_path, "ESRGAN") | |
| move_files(src_path, dest_path) | |
| src_path = os.path.join(models_path, "BSRGAN") | |
| dest_path = os.path.join(models_path, "ESRGAN") | |
| move_files(src_path, dest_path, ".pth") | |
| src_path = os.path.join(root_path, "gfpgan") | |
| dest_path = os.path.join(models_path, "GFPGAN") | |
| move_files(src_path, dest_path) | |
| src_path = os.path.join(root_path, "SwinIR") | |
| dest_path = os.path.join(models_path, "SwinIR") | |
| move_files(src_path, dest_path) | |
| src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/") | |
| dest_path = os.path.join(models_path, "LDSR") | |
| move_files(src_path, dest_path) | |
| def move_files(src_path: str, dest_path: str, ext_filter: str = None): | |
| try: | |
| if not os.path.exists(dest_path): | |
| os.makedirs(dest_path) | |
| if os.path.exists(src_path): | |
| for file in os.listdir(src_path): | |
| fullpath = os.path.join(src_path, file) | |
| if os.path.isfile(fullpath): | |
| if ext_filter is not None: | |
| if ext_filter not in file: | |
| continue | |
| print(f"Moving {file} from {src_path} to {dest_path}.") | |
| try: | |
| shutil.move(fullpath, dest_path) | |
| except: | |
| pass | |
| if len(os.listdir(src_path)) == 0: | |
| print(f"Removing empty folder: {src_path}") | |
| shutil.rmtree(src_path, True) | |
| except: | |
| pass | |
| builtin_upscaler_classes = [] | |
| forbidden_upscaler_classes = set() | |
| def list_builtin_upscalers(): | |
| load_upscalers() | |
| builtin_upscaler_classes.clear() | |
| builtin_upscaler_classes.extend(Upscaler.__subclasses__()) | |
| def forbid_loaded_nonbuiltin_upscalers(): | |
| for cls in Upscaler.__subclasses__(): | |
| if cls not in builtin_upscaler_classes: | |
| forbidden_upscaler_classes.add(cls) | |
| def load_upscalers(): | |
| # We can only do this 'magic' method to dynamically load upscalers if they are referenced, | |
| # so we'll try to import any _model.py files before looking in __subclasses__ | |
| modules_dir = os.path.join(shared.script_path, "modules") | |
| for file in os.listdir(modules_dir): | |
| if "_model.py" in file: | |
| model_name = file.replace("_model.py", "") | |
| full_model = f"modules.{model_name}_model" | |
| try: | |
| importlib.import_module(full_model) | |
| except: | |
| pass | |
| datas = [] | |
| commandline_options = vars(shared.cmd_opts) | |
| for cls in Upscaler.__subclasses__(): | |
| if cls in forbidden_upscaler_classes: | |
| continue | |
| name = cls.__name__ | |
| cmd_name = f"{name.lower().replace('upscaler', '')}_models_path" | |
| scaler = cls(commandline_options.get(cmd_name, None)) | |
| datas += scaler.scalers | |
| shared.sd_upscalers = sorted( | |
| datas, | |
| # Special case for UpscalerNone keeps it at the beginning of the list. | |
| key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else "" | |
| ) | |