| | import re
|
| | from functools import lru_cache
|
| |
|
| | from PIL import Image
|
| |
|
| | from modules import devices, errors, modelloader
|
| | from modules.shared import opts
|
| | from modules.upscaler import Upscaler, UpscalerData
|
| | from modules.upscaler_utils import upscale_with_model
|
| | from modules_forge.forge_util import prepare_free_memory
|
| |
|
| |
|
| | PREFER_HALF = opts.prefer_fp16_upscalers
|
| | if PREFER_HALF:
|
| | print("[Upscalers] Prefer Half-Precision:", PREFER_HALF)
|
| |
|
| |
|
| | class UpscalerESRGAN(Upscaler):
|
| | def __init__(self, dirname: str):
|
| | self.user_path = dirname
|
| | self.model_path = dirname
|
| | super().__init__(True)
|
| |
|
| | self.name = "ESRGAN"
|
| | self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
|
| | self.model_name = "ESRGAN"
|
| | self.scalers = []
|
| |
|
| | model_paths = self.find_models(ext_filter=[".pt", ".pth", ".safetensors"])
|
| | if len(model_paths) == 0:
|
| | scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
|
| | self.scalers.append(scaler_data)
|
| |
|
| | for file in model_paths:
|
| | if file.startswith("http"):
|
| | name = self.model_name
|
| | else:
|
| | name = modelloader.friendly_name(file)
|
| |
|
| | if match := re.search(r"(\d)[xX]|[xX](\d)", name):
|
| | scale = int(match.group(1) or match.group(2))
|
| | else:
|
| | scale = 4
|
| |
|
| | scaler_data = UpscalerData(name, file, self, scale)
|
| | self.scalers.append(scaler_data)
|
| |
|
| | def do_upscale(self, img: Image.Image, selected_model: str):
|
| | prepare_free_memory()
|
| | try:
|
| | model = self.load_model(selected_model)
|
| | except Exception:
|
| | errors.report(f"Unable to load {selected_model}", exc_info=True)
|
| | return img
|
| | return upscale_with_model(
|
| | model=model,
|
| | img=img,
|
| | tile_size=opts.ESRGAN_tile,
|
| | tile_overlap=opts.ESRGAN_tile_overlap,
|
| | )
|
| |
|
| | @lru_cache(maxsize=4, typed=False)
|
| | def load_model(self, path: str):
|
| | if not path.startswith("http"):
|
| | filename = path
|
| | else:
|
| | filename = modelloader.load_file_from_url(
|
| | url=path,
|
| | model_dir=self.model_download_path,
|
| | file_name=path.rsplit("/", 1)[-1],
|
| | )
|
| |
|
| | model = modelloader.load_spandrel_model(filename, device="cpu", prefer_half=PREFER_HALF)
|
| | model.to(devices.device_esrgan)
|
| | return model
|
| |
|
