import math import random import torch from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, StyleGAN2Generator) from basicsr.ops.fused_act import FusedLeakyReLU from basicsr.utils.registry import ARCH_REGISTRY from torch import nn from torch.nn import functional as F class StyleGAN2GeneratorSFT(StyleGAN2Generator): """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). 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. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. 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, resample_kernel=(1, 3, 3, 1), lr_mlp=0.01, narrow=1, sft_half=False): super(StyleGAN2GeneratorSFT, self).__init__( out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, resample_kernel=resample_kernel, lr_mlp=lr_mlp, 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 StyleGAN2GeneratorSFT. 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 ConvUpLayer(nn.Module): """Convolutional upsampling layer. It uses bilinear upsampler + Conv. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. stride (int): Stride of the convolution. Default: 1 padding (int): Zero-padding added to both sides of the input. Default: 0. bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. bias_init_val (float): Bias initialized value. Default: 0. activate (bool): Whether use activateion. Default: True. """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0, activate=True): super(ConvUpLayer, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding # self.scale is used to scale the convolution weights, which is related to the common initializations. self.scale = 1 / math.sqrt(in_channels * kernel_size**2) self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) if bias and not activate: self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) else: self.register_parameter('bias', None) # activation if activate: if bias: self.activation = FusedLeakyReLU(out_channels) else: self.activation = ScaledLeakyReLU(0.2) else: self.activation = None def forward(self, x): # bilinear upsample out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) # conv out = F.conv2d( out, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding, ) # activation if self.activation is not None: out = self.activation(out) return out class ResUpBlock(nn.Module): """Residual block with upsampling. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. """ def __init__(self, in_channels, out_channels): super(ResUpBlock, self).__init__() self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True) self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False) def forward(self, x): out = self.conv1(x) out = self.conv2(out) skip = self.skip(x) out = (out + skip) / math.sqrt(2) return out @ARCH_REGISTRY.register() class GFPGANv1(nn.Module): """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. 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. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). 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. lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. 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, resample_kernel=(1, 3, 3, 1), decoder_load_path=None, fix_decoder=True, # for stylegan decoder num_mlp=8, lr_mlp=0.01, input_is_latent=False, different_w=False, narrow=1, sft_half=False): super(GFPGANv1, 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 = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True) # 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, resample_kernel)) in_channels = out_channels self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True) # 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(ResUpBlock(in_channels, out_channels)) in_channels = out_channels # to RGB self.toRGB = nn.ModuleList() for i in range(3, self.log_size + 1): self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0)) 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 = EqualLinear( channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None) # the decoder: stylegan2 generator with SFT modulations self.stylegan_decoder = StyleGAN2GeneratorSFT( out_size=out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, resample_kernel=resample_kernel, lr_mlp=lr_mlp, 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( EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), ScaledLeakyReLU(0.2), EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1))) self.condition_shift.append( nn.Sequential( EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), ScaledLeakyReLU(0.2), EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0))) def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): """Forward function for GFPGANv1. 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 = self.conv_body_first(x) for i in range(self.log_size - 2): feat = self.conv_body_down[i](feat) unet_skips.insert(0, feat) feat = self.final_conv(feat) # 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 @ARCH_REGISTRY.register() class FacialComponentDiscriminator(nn.Module): """Facial component (eyes, mouth, noise) discriminator used in GFPGAN. """ def __init__(self): super(FacialComponentDiscriminator, self).__init__() # It now uses a VGG-style architectrue with fixed model size self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) def forward(self, x, return_feats=False): """Forward function for FacialComponentDiscriminator. Args: x (Tensor): Input images. return_feats (bool): Whether to return intermediate features. Default: False. """ feat = self.conv1(x) feat = self.conv3(self.conv2(feat)) rlt_feats = [] if return_feats: rlt_feats.append(feat.clone()) feat = self.conv5(self.conv4(feat)) if return_feats: rlt_feats.append(feat.clone()) out = self.final_conv(feat) if return_feats: return out, rlt_feats else: return out, None