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| from __future__ import annotations | |
| 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_file_from_url( | |
| url: str, | |
| *, | |
| model_dir: str, | |
| progress: bool = True, | |
| file_name: str | None = None, | |
| ) -> str: | |
| """Download a file from `url` into `model_dir`, using the file present if possible. | |
| Returns the path to the downloaded file. | |
| """ | |
| os.makedirs(model_dir, exist_ok=True) | |
| if not file_name: | |
| parts = urlparse(url) | |
| file_name = os.path.basename(parts.path) | |
| cached_file = os.path.abspath(os.path.join(model_dir, file_name)) | |
| if not os.path.exists(cached_file): | |
| print(f'Downloading: "{url}" to {cached_file}\n') | |
| from torch.hub import download_url_to_file | |
| download_url_to_file(url, cached_file, progress=progress) | |
| return cached_file | |
| 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 = [] | |
| 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: | |
| for full_path in shared.walk_files(place, allowed_extensions=ext_filter): | |
| 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 full_path not in output: | |
| output.append(full_path) | |
| if model_url is not None and len(output) == 0: | |
| if download_name is not None: | |
| output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name)) | |
| else: | |
| output.append(model_url) | |
| except Exception: | |
| pass | |
| return output | |
| def friendly_name(file: str): | |
| if file.startswith("http"): | |
| 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: | |
| os.makedirs(dest_path, exist_ok=True) | |
| 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 Exception: | |
| pass | |
| if len(os.listdir(src_path)) == 0: | |
| print(f"Removing empty folder: {src_path}") | |
| shutil.rmtree(src_path, True) | |
| except Exception: | |
| pass | |
| 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 Exception: | |
| pass | |
| datas = [] | |
| commandline_options = vars(shared.cmd_opts) | |
| # some of upscaler classes will not go away after reloading their modules, and we'll end | |
| # up with two copies of those classes. The newest copy will always be the last in the list, | |
| # so we go from end to beginning and ignore duplicates | |
| used_classes = {} | |
| for cls in reversed(Upscaler.__subclasses__()): | |
| classname = str(cls) | |
| if classname not in used_classes: | |
| used_classes[classname] = cls | |
| for cls in reversed(used_classes.values()): | |
| name = cls.__name__ | |
| cmd_name = f"{name.lower().replace('upscaler', '')}_models_path" | |
| commandline_model_path = commandline_options.get(cmd_name, None) | |
| scaler = cls(commandline_model_path) | |
| scaler.user_path = commandline_model_path | |
| scaler.model_download_path = commandline_model_path or scaler.model_path | |
| 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 "" | |
| ) | |