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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.utils 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
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