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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 = []
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:
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 Exception:
pass
if len(os.listdir(src_path)) == 0:
print(f"Removing empty folder: {src_path}")
shutil.rmtree(src_path, True)
except Exception:
pass
builtin_upscaler_classes = []
forbidden_upscaler_classes = set()
def list_builtin_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 Exception:
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 ""
)
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