support denoise strength for realesr-general-x4v3
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Xintao
@@ -3,6 +3,7 @@ import cv2
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import glob
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import os
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from basicsr.utils.download_util import load_file_from_url
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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@@ -19,10 +20,18 @@ def main():
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type=str,
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default='RealESRGAN_x4plus',
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help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
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'realesr-animevideov3 | realesr-general-x4v3 | realesr-general-wdn-x4v3'))
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'realesr-animevideov3 | realesr-general-x4v3'))
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parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
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parser.add_argument('--model_path', type=str, default=None, help='Model path')
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parser.add_argument(
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'-dn',
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'--denoise_strength',
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type=float,
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default=0.5,
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help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
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'Only used for the realesr-general-x4v3 model'))
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parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
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parser.add_argument(
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'--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
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parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
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parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
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parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
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@@ -47,36 +56,58 @@ def main():
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# determine models according to model names
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args.model_name = args.model_name.split('.')[0]
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if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model
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if args.model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks
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file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
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elif args.model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth'
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elif args.model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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netscale = 4
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elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model
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file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'
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elif args.model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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netscale = 2
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elif args.model_name in ['realesr-animevideov3']: # x4 VGG-style model (XS size)
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file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'
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elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
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netscale = 4
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elif args.model_name in ['realesr-general-x4v3', 'realesr-general-wdn-x4v3']: # x4 VGG-style model (S size)
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file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth'
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elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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netscale = 4
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file_url = [
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
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]
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# determine model paths
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if args.model_path is not None:
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model_path = args.model_path
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else:
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model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
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if not os.path.isfile(model_path):
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model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
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if not os.path.isfile(model_path):
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raise ValueError(f'Model {args.model_name} does not exist.')
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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for url in file_url:
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# model_path will be updated
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model_path = load_file_from_url(
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url=url, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
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# use dni to control the denoise strength
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dni_weight = None
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if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
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wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
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model_path = [model_path, wdn_model_path]
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dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
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# restorer
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=model_path,
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dni_weight=dni_weight,
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model=model,
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tile=args.tile,
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tile_pad=args.tile_pad,
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@@ -29,6 +29,7 @@ class RealESRGANer():
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def __init__(self,
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scale,
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model_path,
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dni_weight=None,
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model=None,
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tile=0,
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tile_pad=10,
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@@ -49,22 +50,44 @@ class RealESRGANer():
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f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
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else:
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
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if isinstance(model_path, list):
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# dni
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assert len(model_path) == len(dni_weight), 'model_path and dni_weight should have the save length.'
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loadnet = self.dni(model_path[0], model_path[1], dni_weight)
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else:
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# if the model_path starts with https, it will first download models to the folder: realesrgan/weights
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if model_path.startswith('https://'):
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model_path = load_file_from_url(
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url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
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url=model_path,
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model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'),
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progress=True,
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file_name=None)
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loadnet = torch.load(model_path, map_location=torch.device('cpu'))
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# prefer to use params_ema
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if 'params_ema' in loadnet:
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keyname = 'params_ema'
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else:
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keyname = 'params'
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model.load_state_dict(loadnet[keyname], strict=True)
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model.eval()
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self.model = model.to(self.device)
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if self.half:
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self.model = self.model.half()
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def dni(self, net_a, net_b, dni_weight, key='params', loc='cpu'):
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"""Deep network interpolation.
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``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
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"""
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net_a = torch.load(net_a, map_location=torch.device(loc))
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net_b = torch.load(net_b, map_location=torch.device(loc))
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for k, v_a in net_a[key].items():
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net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
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return net_a
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def pre_process(self, img):
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"""Pre-process, such as pre-pad and mod pad, so that the images can be divisible
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"""
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