import os.path import sys import traceback import PIL.Image import numpy as np import torch from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import shared, modelloader from modules.paths import models_path from modules.scunet_model_arch import SCUNet as net class UpscalerScuNET(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "ScuNET" self.model_path = os.path.join(models_path, self.name) self.model_name = "ScuNET GAN" self.model_name2 = "ScuNET PSNR" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" self.user_path = dirname super().__init__() model_paths = self.find_models(ext_filter=[".pth"]) scalers = [] add_model2 = True for file in model_paths: if "http" in file: name = self.model_name else: name = modelloader.friendly_name(file) if name == self.model_name2 or file == self.model_url2: add_model2 = False try: scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) scalers.append(scaler_data) except Exception: print(f"Error loading ScuNET model: {file}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) if add_model2: scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) scalers.append(scaler_data2) self.scalers = scalers def do_upscale(self, img: PIL.Image, selected_file): torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: return img device = shared.device img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(shared.device) img = img.to(device) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() output = 255. * np.moveaxis(output, 0, 2) output = output.astype(np.uint8) output = output[:, :, ::-1] torch.cuda.empty_cache() return PIL.Image.fromarray(output, 'RGB') def load_model(self, path: str): device = shared.device if "http" in path: filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, progress=True) else: filename = path if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr) return None model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) model.load_state_dict(torch.load(filename), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False model = model.to(device) return model