170 lines
6.0 KiB
Python
170 lines
6.0 KiB
Python
from __future__ import annotations
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import importlib
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import logging
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import os
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from typing import TYPE_CHECKING
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from urllib.parse import urlparse
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import torch
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from modules import shared
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from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
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if TYPE_CHECKING:
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import spandrel
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logger = logging.getLogger(__name__)
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def load_file_from_url(
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url: str,
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*,
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model_dir: str,
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progress: bool = True,
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file_name: str | None = None,
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) -> str:
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"""Download a file from `url` into `model_dir`, using the file present if possible.
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Returns the path to the downloaded file.
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"""
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os.makedirs(model_dir, exist_ok=True)
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if not file_name:
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parts = urlparse(url)
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file_name = os.path.basename(parts.path)
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cached_file = os.path.abspath(os.path.join(model_dir, file_name))
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if not os.path.exists(cached_file):
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print(f'Downloading: "{url}" to {cached_file}\n')
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from torch.hub import download_url_to_file
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download_url_to_file(url, cached_file, progress=progress)
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return cached_file
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def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=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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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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for full_path in shared.walk_files(place, allowed_extensions=ext_filter):
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if os.path.islink(full_path) and not os.path.exists(full_path):
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print(f"Skipping broken symlink: {full_path}")
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continue
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if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
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continue
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if full_path 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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output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
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else:
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output.append(model_url)
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except Exception:
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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 file.startswith("http"):
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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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return model_name
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def load_upscalers():
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# We can only do this 'magic' method to dynamically load upscalers if they are referenced,
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# so we'll try to import any _model.py files before looking in __subclasses__
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modules_dir = os.path.join(shared.script_path, "modules")
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for file in os.listdir(modules_dir):
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if "_model.py" in file:
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model_name = file.replace("_model.py", "")
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full_model = f"modules.{model_name}_model"
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try:
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importlib.import_module(full_model)
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except Exception:
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pass
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data = []
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commandline_options = vars(shared.cmd_opts)
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# some of upscaler classes will not go away after reloading their modules, and we'll end
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# up with two copies of those classes. The newest copy will always be the last in the list,
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# so we go from end to beginning and ignore duplicates
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used_classes = {}
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for cls in reversed(Upscaler.__subclasses__()):
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classname = str(cls)
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if classname not in used_classes:
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used_classes[classname] = cls
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for cls in reversed(used_classes.values()):
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name = cls.__name__
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cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
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commandline_model_path = commandline_options.get(cmd_name, None)
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scaler = cls(commandline_model_path)
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scaler.user_path = commandline_model_path
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scaler.model_download_path = commandline_model_path or scaler.model_path
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data += scaler.scalers
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shared.sd_upscalers = sorted(
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data,
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# Special case for UpscalerNone keeps it at the beginning of the list.
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key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
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)
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def load_spandrel_model(
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path: str | os.PathLike,
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*,
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device: str | torch.device | None,
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prefer_half: bool = False,
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dtype: str | torch.dtype | None = None,
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expected_architecture: str | None = None,
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) -> spandrel.ModelDescriptor:
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import spandrel
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model_descriptor = spandrel.ModelLoader(device=device).load_from_file(str(path))
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if expected_architecture and model_descriptor.architecture != expected_architecture:
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logger.warning(
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f"Model {path!r} is not a {expected_architecture!r} model (got {model_descriptor.architecture!r})",
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)
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half = False
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if prefer_half:
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if model_descriptor.supports_half:
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model_descriptor.model.half()
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half = True
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else:
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logger.info("Model %s does not support half precision, ignoring --half", path)
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if dtype:
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model_descriptor.model.to(dtype=dtype)
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model_descriptor.model.eval()
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logger.debug(
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"Loaded %s from %s (device=%s, half=%s, dtype=%s)",
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model_descriptor, path, device, half, dtype,
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)
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return model_descriptor
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