mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-07-21 21:01:24 +08:00
126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
from __future__ import annotations
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import logging
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import os
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from urllib.parse import urlparse
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import spandrel
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import spandrel_extra_arches
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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 # noqa
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from modules.util import load_file_from_url # noqa
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spandrel_extra_arches.install()
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logger = logging.getLogger(__name__)
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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, hash_prefix=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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@param hash_prefix: the expected sha256 of the model_url
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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, hash_prefix=hash_prefix))
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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, _ = os.path.splitext(file)
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return model_name
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def load_upscalers():
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from modules.esrgan_model import UpscalerESRGAN
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del shared.sd_upscalers
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commandline_model_path = shared.cmd_opts.esrgan_models_path
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upscaler = UpscalerESRGAN(commandline_model_path)
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upscaler.user_path = commandline_model_path
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upscaler.model_download_path = commandline_model_path or upscaler.model_path
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shared.sd_upscalers = [
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*UpscalerNone().scalers,
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*UpscalerLanczos().scalers,
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*UpscalerNearest().scalers,
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*sorted(upscaler.scalers, key=lambda s: s.name.lower()),
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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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model_descriptor = spandrel.ModelLoader(device=device).load_from_file(str(path))
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arch = model_descriptor.architecture
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logger.info(f'Loaded {arch.name} Model: "{os.path.basename(path)}"')
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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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elif model_descriptor.supports_bfloat16:
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model_descriptor.model.bfloat16()
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half = True
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else:
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logger.warning(f"Model {path} does not support half precision...")
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if dtype:
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model_descriptor.model.to(dtype=dtype)
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logger.debug("Loaded %s from %s (device=%s, half=%s, dtype=%s)", arch, path, device, half, dtype)
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model_descriptor.model.eval()
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return model_descriptor
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