import glob import os import shutil import importlib from urllib.parse import urlparse from basicsr.utils.download_util import load_file_from_url from modules import shared from modules.upscaler import Upscaler 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 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: 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 = datas