224 lines
8.6 KiB
Python
224 lines
8.6 KiB
Python
import os
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import numpy as np
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import torch
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from PIL import Image
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from basicsr.utils.download_util import load_file_from_url
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import modules.esrgan_model_arch as arch
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from modules import shared, modelloader, images, devices
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from modules.upscaler import Upscaler, UpscalerData
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from modules.shared import opts
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def mod2normal(state_dict):
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# this code is copied from https://github.com/victorca25/iNNfer
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if 'conv_first.weight' in state_dict:
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crt_net = {}
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items = []
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for k, v in state_dict.items():
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items.append(k)
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crt_net['model.0.weight'] = state_dict['conv_first.weight']
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crt_net['model.0.bias'] = state_dict['conv_first.bias']
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for k in items.copy():
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if 'RDB' in k:
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ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
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if '.weight' in k:
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ori_k = ori_k.replace('.weight', '.0.weight')
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elif '.bias' in k:
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ori_k = ori_k.replace('.bias', '.0.bias')
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crt_net[ori_k] = state_dict[k]
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items.remove(k)
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crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
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crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
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crt_net['model.3.weight'] = state_dict['upconv1.weight']
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crt_net['model.3.bias'] = state_dict['upconv1.bias']
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crt_net['model.6.weight'] = state_dict['upconv2.weight']
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crt_net['model.6.bias'] = state_dict['upconv2.bias']
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crt_net['model.8.weight'] = state_dict['HRconv.weight']
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crt_net['model.8.bias'] = state_dict['HRconv.bias']
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crt_net['model.10.weight'] = state_dict['conv_last.weight']
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crt_net['model.10.bias'] = state_dict['conv_last.bias']
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state_dict = crt_net
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return state_dict
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def resrgan2normal(state_dict, nb=23):
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# this code is copied from https://github.com/victorca25/iNNfer
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if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
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crt_net = {}
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items = []
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for k, v in state_dict.items():
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items.append(k)
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crt_net['model.0.weight'] = state_dict['conv_first.weight']
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crt_net['model.0.bias'] = state_dict['conv_first.bias']
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for k in items.copy():
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if "rdb" in k:
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ori_k = k.replace('body.', 'model.1.sub.')
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ori_k = ori_k.replace('.rdb', '.RDB')
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if '.weight' in k:
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ori_k = ori_k.replace('.weight', '.0.weight')
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elif '.bias' in k:
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ori_k = ori_k.replace('.bias', '.0.bias')
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crt_net[ori_k] = state_dict[k]
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items.remove(k)
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crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
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crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
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crt_net['model.3.weight'] = state_dict['conv_up1.weight']
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crt_net['model.3.bias'] = state_dict['conv_up1.bias']
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crt_net['model.6.weight'] = state_dict['conv_up2.weight']
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crt_net['model.6.bias'] = state_dict['conv_up2.bias']
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crt_net['model.8.weight'] = state_dict['conv_hr.weight']
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crt_net['model.8.bias'] = state_dict['conv_hr.bias']
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crt_net['model.10.weight'] = state_dict['conv_last.weight']
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crt_net['model.10.bias'] = state_dict['conv_last.bias']
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state_dict = crt_net
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return state_dict
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def infer_params(state_dict):
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# this code is copied from https://github.com/victorca25/iNNfer
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scale2x = 0
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scalemin = 6
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n_uplayer = 0
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plus = False
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for block in list(state_dict):
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parts = block.split(".")
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n_parts = len(parts)
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if n_parts == 5 and parts[2] == "sub":
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nb = int(parts[3])
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elif n_parts == 3:
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part_num = int(parts[1])
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if (part_num > scalemin
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and parts[0] == "model"
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and parts[2] == "weight"):
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scale2x += 1
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if part_num > n_uplayer:
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n_uplayer = part_num
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out_nc = state_dict[block].shape[0]
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if not plus and "conv1x1" in block:
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plus = True
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nf = state_dict["model.0.weight"].shape[0]
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in_nc = state_dict["model.0.weight"].shape[1]
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out_nc = out_nc
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scale = 2 ** scale2x
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return in_nc, out_nc, nf, nb, plus, scale
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class UpscalerESRGAN(Upscaler):
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def __init__(self, dirname):
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self.name = "ESRGAN"
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self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
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self.model_name = "ESRGAN_4x"
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self.scalers = []
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self.user_path = dirname
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super().__init__()
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model_paths = self.find_models(ext_filter=[".pt", ".pth"])
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scalers = []
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if len(model_paths) == 0:
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scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
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scalers.append(scaler_data)
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for file in model_paths:
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if "http" in file:
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name = self.model_name
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else:
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name = modelloader.friendly_name(file)
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scaler_data = UpscalerData(name, file, self, 4)
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self.scalers.append(scaler_data)
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def do_upscale(self, img, selected_model):
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model = self.load_model(selected_model)
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if model is None:
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return img
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model.to(devices.device_esrgan)
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img = esrgan_upscale(model, img)
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return img
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def load_model(self, path: str):
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if "http" in path:
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filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
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file_name="%s.pth" % self.model_name,
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progress=True)
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else:
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filename = path
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if not os.path.exists(filename) or filename is None:
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print("Unable to load %s from %s" % (self.model_path, filename))
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return None
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state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
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if "params_ema" in state_dict:
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state_dict = state_dict["params_ema"]
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elif "params" in state_dict:
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state_dict = state_dict["params"]
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num_conv = 16 if "realesr-animevideov3" in filename else 32
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model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
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model.load_state_dict(state_dict)
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model.eval()
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return model
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if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
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nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
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state_dict = resrgan2normal(state_dict, nb)
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elif "conv_first.weight" in state_dict:
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state_dict = mod2normal(state_dict)
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elif "model.0.weight" not in state_dict:
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raise Exception("The file is not a recognized ESRGAN model.")
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in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
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model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
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model.load_state_dict(state_dict)
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model.eval()
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return model
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def upscale_without_tiling(model, img):
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(devices.device_esrgan)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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output = 255. * np.moveaxis(output, 0, 2)
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output = output.astype(np.uint8)
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output = output[:, :, ::-1]
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return Image.fromarray(output, 'RGB')
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def esrgan_upscale(model, img):
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if opts.ESRGAN_tile == 0:
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return upscale_without_tiling(model, img)
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grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
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newtiles = []
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scale_factor = 1
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for y, h, row in grid.tiles:
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newrow = []
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for tiledata in row:
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x, w, tile = tiledata
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output = upscale_without_tiling(model, tile)
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scale_factor = output.width // tile.width
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newrow.append([x * scale_factor, w * scale_factor, output])
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newtiles.append([y * scale_factor, h * scale_factor, newrow])
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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)
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output = images.combine_grid(newgrid)
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return output
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