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 devices, modelloader from modules.bsrgan_model_arch import RRDBNet class UpscalerBSRGAN(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "BSRGAN" self.model_name = "BSRGAN 4x" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth" self.user_path = dirname super().__init__() model_paths = self.find_models(ext_filter=[".pt", ".pth"]) scalers = [] if len(model_paths) == 0: scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4) scalers.append(scaler_data) for file in model_paths: if "http" in file: name = self.model_name else: name = modelloader.friendly_name(file) try: scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) scalers.append(scaler_data) except Exception: print(f"Error loading BSRGAN model: {file}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) 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 model.to(devices.device_bsrgan) torch.cuda.empty_cache() 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(devices.device_bsrgan) 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): 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(filename) or filename is None: print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr) return None model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network model.load_state_dict(torch.load(filename), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False return model