0dce0df1ee
Yep. Fix gfpgan_model_arch requirement(s). Add Upscaler base class, move from images. Add a lot of methods to Upscaler. Re-work all the child upscalers to be proper classes. Add BSRGAN scaler. Add ldsr_model_arch class, removing the dependency for another repo that just uses regular latent-diffusion stuff. Add one universal method that will always find and load new upscaler models without having to add new "setup_model" calls. Still need to add command line params, but that could probably be automated. Add a "self.scale" property to all Upscalers so the scalers themselves can do "things" in response to the requested upscaling size. Ensure LDSR doesn't get stuck in a longer loop of "upscale/downscale/upscale" as we try to reach the target upscale size. Add typehints for IDE sanity. PEP-8 improvements. Moar.
148 lines
5.5 KiB
Python
148 lines
5.5 KiB
Python
import os
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import shutil
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import importlib
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from urllib.parse import urlparse
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from basicsr.utils.download_util import load_file_from_url
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from modules import shared
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from modules.upscaler import Upscaler
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from modules.paths import script_path, models_path
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def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
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"""
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A one-and done loader to try finding the desired models in specified directories.
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@param download_name: Specify to download from model_url immediately.
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@param model_url: If no other models are found, this will be downloaded on upscale.
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@param model_path: The location to store/find models in.
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@param command_path: A command-line argument to search for models in first.
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@param ext_filter: An optional list of filename extensions to filter by
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@return: A list of paths containing the desired model(s)
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"""
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output = []
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if ext_filter is None:
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ext_filter = []
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try:
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places = []
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if command_path is not None and command_path != model_path:
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pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
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if os.path.exists(pretrained_path):
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print(f"Appending path: {pretrained_path}")
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places.append(pretrained_path)
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elif os.path.exists(command_path):
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places.append(command_path)
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places.append(model_path)
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for place in places:
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if os.path.exists(place):
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for file in os.listdir(place):
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full_path = os.path.join(place, file)
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if os.path.isdir(full_path):
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continue
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if len(ext_filter) != 0:
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model_name, extension = os.path.splitext(file)
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if extension not in ext_filter:
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continue
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if file not in output:
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output.append(full_path)
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if model_url is not None and len(output) == 0:
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if download_name is not None:
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dl = load_file_from_url(model_url, model_path, True, download_name)
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output.append(dl)
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else:
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output.append(model_url)
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except:
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pass
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return output
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def friendly_name(file: str):
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if "http" in file:
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file = urlparse(file).path
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file = os.path.basename(file)
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model_name, extension = os.path.splitext(file)
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model_name = model_name.replace("_", " ").title()
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return model_name
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def cleanup_models():
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# This code could probably be more efficient if we used a tuple list or something to store the src/destinations
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# and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
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# somehow auto-register and just do these things...
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root_path = script_path
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src_path = models_path
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dest_path = os.path.join(models_path, "Stable-diffusion")
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move_files(src_path, dest_path, ".ckpt")
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src_path = os.path.join(root_path, "ESRGAN")
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dest_path = os.path.join(models_path, "ESRGAN")
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move_files(src_path, dest_path)
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src_path = os.path.join(root_path, "gfpgan")
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dest_path = os.path.join(models_path, "GFPGAN")
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move_files(src_path, dest_path)
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src_path = os.path.join(root_path, "SwinIR")
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dest_path = os.path.join(models_path, "SwinIR")
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move_files(src_path, dest_path)
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src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
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dest_path = os.path.join(models_path, "LDSR")
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move_files(src_path, dest_path)
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def move_files(src_path: str, dest_path: str, ext_filter: str = None):
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try:
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if not os.path.exists(dest_path):
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os.makedirs(dest_path)
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if os.path.exists(src_path):
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for file in os.listdir(src_path):
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fullpath = os.path.join(src_path, file)
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if os.path.isfile(fullpath):
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if ext_filter is not None:
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if ext_filter not in file:
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continue
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print(f"Moving {file} from {src_path} to {dest_path}.")
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try:
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shutil.move(fullpath, dest_path)
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except:
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pass
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if len(os.listdir(src_path)) == 0:
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print(f"Removing empty folder: {src_path}")
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shutil.rmtree(src_path, True)
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except:
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pass
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def load_upscalers():
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datas = []
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for cls in Upscaler.__subclasses__():
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name = cls.__name__
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module_name = cls.__module__
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print(f"Class: {name} and {module_name}")
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module = importlib.import_module(module_name)
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class_ = getattr(module, name)
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cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
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print(f"CMD Name: {cmd_name}")
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opt_string = None
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try:
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opt_string = shared.opts.__getattr__(cmd_name)
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except:
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pass
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scaler = class_(opt_string)
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for child in scaler.scalers:
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print(f"Appending {child.name}")
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datas.append(child)
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shared.sd_upscalers = datas
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# for scaler in subclasses:
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# print(f"Found scaler: {type(scaler).__name__}")
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# try:
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# scaler = scaler()
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# for child in scaler.scalers:
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# print(f"Appending {child.name}")
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# datas.append[child]
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# except:
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# pass
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# shared.sd_upscalers = datas
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