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