import os.path import sys import traceback import PIL.Image import numpy as np import torch from tqdm import tqdm from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from scunet_model_arch import SCUNet as net from modules.shared import opts class UpscalerScuNET(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "ScuNET" 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 @staticmethod @torch.no_grad() def tiled_inference(img, model): # test the image tile by tile h, w = img.shape[2:] tile = opts.SCUNET_tile tile_overlap = opts.SCUNET_tile_overlap if tile == 0: return model(img) device = devices.get_device_for('scunet') assert tile % 8 == 0, "tile size should be a multiple of window_size" sf = 1 stride = tile - tile_overlap h_idx_list = list(range(0, h - tile, stride)) + [h - tile] w_idx_list = list(range(0, w - tile, stride)) + [w - tile] E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device) W = torch.zeros_like(E, dtype=devices.dtype, device=device) with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar: for h_idx in h_idx_list: for w_idx in w_idx_list: in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] out_patch = model(in_patch) out_patch_mask = torch.ones_like(out_patch) E[ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf ].add_(out_patch) W[ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf ].add_(out_patch_mask) pbar.update(1) output = E.div_(W) return output def do_upscale(self, img: PIL.Image.Image, selected_file): torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) return img device = devices.get_device_for('scunet') tile = opts.SCUNET_tile h, w = img.height, img.width np_img = np.array(img) np_img = np_img[:, :, ::-1] # RGB to BGR np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore if tile > h or tile > w: _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device) _img[:, :, :h, :w] = torch_img # pad image torch_img = _img torch_output = self.tiled_inference(torch_img, model).squeeze(0) torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() del torch_img, torch_output torch.cuda.empty_cache() output = np_output.transpose((1, 2, 0)) # CHW to HWC output = output[:, :, ::-1] # BGR to RGB return PIL.Image.fromarray((output * 255).astype(np.uint8)) def load_model(self, path: str): device = devices.get_device_for('scunet') 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