import math import random import torch from basicsr.utils.registry import ARCH_REGISTRY from torch import nn from torch.nn import functional as F from .stylegan2_clean_arch import StyleGAN2GeneratorClean class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). It is the clean version without custom compiled CUDA extensions used in StyleGAN2. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False): super(StyleGAN2GeneratorCSFT, self).__init__( out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, narrow=narrow) self.sft_half = sft_half def forward(self, styles, conditions, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False): """Forward function for StyleGAN2GeneratorCSFT. Args: styles (list[Tensor]): Sample codes of styles. conditions (list[Tensor]): SFT conditions to generators. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): The truncation ratio. Default: 1. truncation_latent (Tensor | None): The truncation latent tensor. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) styles = style_truncation # get style latents with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs): out = conv1(out, latent[:, i], noise=noise1) # the conditions may have fewer levels if i < len(conditions): # SFT part to combine the conditions if self.sft_half: # only apply SFT to half of the channels out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) out_sft = out_sft * conditions[i - 1] + conditions[i] out = torch.cat([out_same, out_sft], dim=1) else: # apply SFT to all the channels out = out * conditions[i - 1] + conditions[i] out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space i += 2 image = skip if return_latents: return image, latent else: return image, None class ResBlock(nn.Module): """Residual block with bilinear upsampling/downsampling. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. """ def __init__(self, in_channels, out_channels, mode='down'): super(ResBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) if mode == 'down': self.scale_factor = 0.5 elif mode == 'up': self.scale_factor = 2 def forward(self, x): out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) # upsample/downsample out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) # skip x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) skip = self.skip(x) out = out + skip return out @ARCH_REGISTRY.register() class GFPGANv1Clean(nn.Module): """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. It is the clean version without custom compiled CUDA extensions used in StyleGAN2. Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. fix_decoder (bool): Whether to fix the decoder. Default: True. num_mlp (int): Layer number of MLP style layers. Default: 8. input_is_latent (bool): Whether input is latent style. Default: False. different_w (bool): Whether to use different latent w for different layers. Default: False. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, out_size, num_style_feat=512, channel_multiplier=1, decoder_load_path=None, fix_decoder=True, # for stylegan decoder num_mlp=8, input_is_latent=False, different_w=False, narrow=1, sft_half=False): super(GFPGANv1Clean, self).__init__() self.input_is_latent = input_is_latent self.different_w = different_w self.num_style_feat = num_style_feat unet_narrow = narrow * 0.5 # by default, use a half of input channels channels = { '4': int(512 * unet_narrow), '8': int(512 * unet_narrow), '16': int(512 * unet_narrow), '32': int(512 * unet_narrow), '64': int(256 * channel_multiplier * unet_narrow), '128': int(128 * channel_multiplier * unet_narrow), '256': int(64 * channel_multiplier * unet_narrow), '512': int(32 * channel_multiplier * unet_narrow), '1024': int(16 * channel_multiplier * unet_narrow) } self.log_size = int(math.log(out_size, 2)) first_out_size = 2**(int(math.log(out_size, 2))) self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1) # downsample in_channels = channels[f'{first_out_size}'] self.conv_body_down = nn.ModuleList() for i in range(self.log_size, 2, -1): out_channels = channels[f'{2**(i - 1)}'] self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down')) in_channels = out_channels self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1) # upsample in_channels = channels['4'] self.conv_body_up = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f'{2**i}'] self.conv_body_up.append(ResBlock(in_channels, out_channels, mode='up')) in_channels = out_channels # to RGB self.toRGB = nn.ModuleList() for i in range(3, self.log_size + 1): self.toRGB.append(nn.Conv2d(channels[f'{2**i}'], 3, 1)) if different_w: linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat else: linear_out_channel = num_style_feat self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel) # the decoder: stylegan2 generator with SFT modulations self.stylegan_decoder = StyleGAN2GeneratorCSFT( out_size=out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, narrow=narrow, sft_half=sft_half) # load pre-trained stylegan2 model if necessary if decoder_load_path: self.stylegan_decoder.load_state_dict( torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) # fix decoder without updating params if fix_decoder: for _, param in self.stylegan_decoder.named_parameters(): param.requires_grad = False # for SFT modulations (scale and shift) self.condition_scale = nn.ModuleList() self.condition_shift = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f'{2**i}'] if sft_half: sft_out_channels = out_channels else: sft_out_channels = out_channels * 2 self.condition_scale.append( nn.Sequential( nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) self.condition_shift.append( nn.Sequential( nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs): """Forward function for GFPGANv1Clean. Args: x (Tensor): Input images. return_latents (bool): Whether to return style latents. Default: False. return_rgb (bool): Whether return intermediate rgb images. Default: True. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. """ conditions = [] unet_skips = [] out_rgbs = [] # encoder feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) for i in range(self.log_size - 2): feat = self.conv_body_down[i](feat) unet_skips.insert(0, feat) feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) # style code style_code = self.final_linear(feat.view(feat.size(0), -1)) if self.different_w: style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) # decode for i in range(self.log_size - 2): # add unet skip feat = feat + unet_skips[i] # ResUpLayer feat = self.conv_body_up[i](feat) # generate scale and shift for SFT layers scale = self.condition_scale[i](feat) conditions.append(scale.clone()) shift = self.condition_shift[i](feat) conditions.append(shift.clone()) # generate rgb images if return_rgb: out_rgbs.append(self.toRGB[i](feat)) # decoder image, _ = self.stylegan_decoder([style_code], conditions, return_latents=return_latents, input_is_latent=self.input_is_latent, randomize_noise=randomize_noise) return image, out_rgbs