import os import numpy as np import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url import modules.esrgam_model_arch as arch from modules import shared, modelloader, images, devices from modules.paths import models_path from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts def fix_model_layers(crt_model, pretrained_net): # this code is adapted from https://github.com/xinntao/ESRGAN if 'conv_first.weight' in pretrained_net: return pretrained_net if 'model.0.weight' not in pretrained_net: is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"] if is_realesrgan: raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.") else: raise Exception("The file is not a ESRGAN model.") crt_net = crt_model.state_dict() load_net_clean = {} for k, v in pretrained_net.items(): if k.startswith('module.'): load_net_clean[k[7:]] = v else: load_net_clean[k] = v pretrained_net = load_net_clean tbd = [] for k, v in crt_net.items(): tbd.append(k) # directly copy for k, v in crt_net.items(): if k in pretrained_net and pretrained_net[k].size() == v.size(): crt_net[k] = pretrained_net[k] tbd.remove(k) crt_net['conv_first.weight'] = pretrained_net['model.0.weight'] crt_net['conv_first.bias'] = pretrained_net['model.0.bias'] for k in tbd.copy(): if 'RDB' in k: ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') if '.weight' in k: ori_k = ori_k.replace('.weight', '.0.weight') elif '.bias' in k: ori_k = ori_k.replace('.bias', '.0.bias') crt_net[k] = pretrained_net[ori_k] tbd.remove(k) crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight'] crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias'] crt_net['upconv1.weight'] = pretrained_net['model.3.weight'] crt_net['upconv1.bias'] = pretrained_net['model.3.bias'] crt_net['upconv2.weight'] = pretrained_net['model.6.weight'] crt_net['upconv2.bias'] = pretrained_net['model.6.bias'] crt_net['HRconv.weight'] = pretrained_net['model.8.weight'] crt_net['HRconv.bias'] = pretrained_net['model.8.bias'] crt_net['conv_last.weight'] = pretrained_net['model.10.weight'] crt_net['conv_last.bias'] = pretrained_net['model.10.bias'] return crt_net class UpscalerESRGAN(Upscaler): def __init__(self, dirname): self.name = "ESRGAN" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth" self.model_name = "ESRGAN_4x" self.scalers = [] self.user_path = dirname self.model_path = os.path.join(models_path, self.name) super().__init__() model_paths = self.find_models(ext_filter=[".pt", ".pth"]) scalers = [] if len(model_paths) == 0: scaler_data = 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) scaler_data = UpscalerData(name, file, self, 4) self.scalers.append(scaler_data) def do_upscale(self, img, selected_model): model = self.load_model(selected_model) if model is None: return img model.to(devices.device_esrgan) img = esrgan_upscale(model, img) return img 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.model_name, progress=True) else: filename = path if not os.path.exists(filename) or filename is None: print("Unable to load %s from %s" % (self.model_path, filename)) return None pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) pretrained_net = fix_model_layers(crt_model, pretrained_net) crt_model.load_state_dict(pretrained_net) crt_model.eval() return crt_model def upscale_without_tiling(model, img): 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_esrgan) 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] return Image.fromarray(output, 'RGB') def esrgan_upscale(model, img): if opts.ESRGAN_tile == 0: return upscale_without_tiling(model, img) grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap) newtiles = [] scale_factor = 1 for y, h, row in grid.tiles: newrow = [] for tiledata in row: x, w, tile = tiledata output = upscale_without_tiling(model, tile) scale_factor = output.width // tile.width newrow.append([x * scale_factor, w * scale_factor, output]) newtiles.append([y * scale_factor, h * scale_factor, newrow]) newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor) output = images.combine_grid(newgrid) return output