import torch import numpy as np import math from torch import nn from model.stylegan.model import ConvLayer, EqualLinear, Generator, ResBlock from model.dualstylegan import AdaptiveInstanceNorm, AdaResBlock, DualStyleGAN import torch.nn.functional as F # IC-GAN: stylegan discriminator class ConditionalDiscriminator(nn.Module): def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], use_condition=False, style_num=None): super().__init__() channels = { 4: 512, 8: 512, 16: 512, 32: 512, 64: 256 * channel_multiplier, 128: 128 * channel_multiplier, 256: 64 * channel_multiplier, 512: 32 * channel_multiplier, 1024: 16 * channel_multiplier, } convs = [ConvLayer(3, channels[size], 1)] log_size = int(math.log(size, 2)) in_channel = channels[size] for i in range(log_size, 2, -1): out_channel = channels[2 ** (i - 1)] convs.append(ResBlock(in_channel, out_channel, blur_kernel)) in_channel = out_channel self.convs = nn.Sequential(*convs) self.stddev_group = 4 self.stddev_feat = 1 self.use_condition = use_condition if self.use_condition: self.condition_dim = 128 # map style degree to 64-dimensional vector self.label_mapper = nn.Sequential( nn.Linear(1, 64), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Linear(64, 64), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Linear(64, self.condition_dim//2), ) # map style code index to 64-dimensional vector self.style_mapper = nn.Embedding(style_num, self.condition_dim-self.condition_dim//2) else: self.condition_dim = 1 self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) self.final_linear = nn.Sequential( EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"), EqualLinear(channels[4], self.condition_dim), ) def forward(self, input, degree_label=None, style_ind=None): out = self.convs(input) batch, channel, height, width = out.shape group = min(batch, self.stddev_group) stddev = out.view( group, -1, self.stddev_feat, channel // self.stddev_feat, height, width ) stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) stddev = stddev.repeat(group, 1, height, width) out = torch.cat([out, stddev], 1) out = self.final_conv(out) out = out.view(batch, -1) if self.use_condition: h = self.final_linear(out) condition = torch.cat((self.label_mapper(degree_label), self.style_mapper(style_ind)), dim=1) out = (h * condition).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.condition_dim)) else: out = self.final_linear(out) return out class VToonifyResBlock(nn.Module): def __init__(self, fin): super().__init__() self.conv = nn.Conv2d(fin, fin, 3, 1, 1) self.conv2 = nn.Conv2d(fin, fin, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): out = self.lrelu(self.conv(x)) out = self.lrelu(self.conv2(out)) out = (out + x) / math.sqrt(2) return out class Fusion(nn.Module): def __init__(self, in_channels, skip_channels, out_channels): super().__init__() # create conv layers self.conv = nn.Conv2d(in_channels + skip_channels, out_channels, 3, 1, 1, bias=True) self.norm = AdaptiveInstanceNorm(in_channels + skip_channels, 128) self.conv2 = nn.Conv2d(in_channels + skip_channels, 1, 3, 1, 1, bias=True) #''' self.linear = nn.Sequential( nn.Linear(1, 64), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Linear(64, 128), nn.LeakyReLU(negative_slope=0.2, inplace=True) ) def forward(self, f_G, f_E, d_s=1): # label of style degree label = self.linear(torch.zeros(f_G.size(0),1).to(f_G.device) + d_s) out = torch.cat([f_G, abs(f_G-f_E)], dim=1) m_E = (F.relu(self.conv2(self.norm(out, label)))).tanh() f_out = self.conv(torch.cat([f_G, f_E * m_E], dim=1)) return f_out, m_E class VToonify(nn.Module): def __init__(self, in_size=256, out_size=1024, img_channels=3, style_channels=512, num_mlps=8, channel_multiplier=2, num_res_layers=6, backbone = 'dualstylegan', ): super().__init__() self.backbone = backbone if self.backbone == 'dualstylegan': # DualStyleGAN, with weights being fixed self.generator = DualStyleGAN(out_size, style_channels, num_mlps, channel_multiplier) else: # StyleGANv2, with weights being fixed self.generator = Generator(out_size, style_channels, num_mlps, channel_multiplier) self.in_size = in_size self.style_channels = style_channels channels = self.generator.channels # encoder num_styles = int(np.log2(out_size)) * 2 - 2 encoder_res = [2**i for i in range(int(np.log2(in_size)), 4, -1)] self.encoder = nn.ModuleList() self.encoder.append( nn.Sequential( nn.Conv2d(img_channels+19, 32, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(32, channels[in_size], 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True))) for res in encoder_res: in_channels = channels[res] if res > 32: out_channels = channels[res // 2] block = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, 2, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True)) self.encoder.append(block) else: layers = [] for _ in range(num_res_layers): layers.append(VToonifyResBlock(in_channels)) self.encoder.append(nn.Sequential(*layers)) block = nn.Conv2d(in_channels, img_channels, 1, 1, 0, bias=True) self.encoder.append(block) # trainable fusion module self.fusion_out = nn.ModuleList() self.fusion_skip = nn.ModuleList() for res in encoder_res[::-1]: num_channels = channels[res] if self.backbone == 'dualstylegan': self.fusion_out.append( Fusion(num_channels, num_channels, num_channels)) else: self.fusion_out.append( nn.Conv2d(num_channels * 2, num_channels, 3, 1, 1, bias=True)) self.fusion_skip.append( nn.Conv2d(num_channels + 3, 3, 3, 1, 1, bias=True)) # Modified ModRes blocks in DualStyleGAN, with weights being fixed if self.backbone == 'dualstylegan': self.res = nn.ModuleList() self.res.append(AdaResBlock(self.generator.channels[2 ** 2])) # for conv1, no use in this model for i in range(3, 6): out_channel = self.generator.channels[2 ** i] self.res.append(AdaResBlock(out_channel, dilation=2**(5-i))) self.res.append(AdaResBlock(out_channel, dilation=2**(5-i))) def forward(self, x, style, d_s=None, return_mask=False, return_feat=False): # map style to W+ space if style is not None and style.ndim < 3: if self.backbone == 'dualstylegan': resstyles = self.generator.style(style).unsqueeze(1).repeat(1, self.generator.n_latent, 1) adastyles = style.unsqueeze(1).repeat(1, self.generator.n_latent, 1) elif style is not None: nB, nL, nD = style.shape if self.backbone == 'dualstylegan': resstyles = self.generator.style(style.reshape(nB*nL, nD)).reshape(nB, nL, nD) adastyles = style if self.backbone == 'dualstylegan': adastyles = adastyles.clone() for i in range(7, self.generator.n_latent): adastyles[:, i] = self.generator.res[i](adastyles[:, i]) # obtain multi-scale content features feat = x encoder_features = [] # downsampling conv parts of E for block in self.encoder[:-2]: feat = block(feat) encoder_features.append(feat) encoder_features = encoder_features[::-1] # Resblocks in E for ii, block in enumerate(self.encoder[-2]): feat = block(feat) # adjust Resblocks with ModRes blocks if self.backbone == 'dualstylegan': feat = self.res[ii+1](feat, resstyles[:, ii+1], d_s) # the last-layer feature of E (inputs of backbone) out = feat skip = self.encoder[-1](feat) if return_feat: return out, skip # 32x32 ---> higher res _index = 1 m_Es = [] for conv1, conv2, to_rgb in zip( self.stylegan().convs[6::2], self.stylegan().convs[7::2], self.stylegan().to_rgbs[3:]): # pass the mid-layer features of E to the corresponding resolution layers of G if 2 ** (5+((_index-1)//2)) <= self.in_size: fusion_index = (_index - 1) // 2 f_E = encoder_features[fusion_index] if self.backbone == 'dualstylegan': out, m_E = self.fusion_out[fusion_index](out, f_E, d_s) skip = self.fusion_skip[fusion_index](torch.cat([skip, f_E*m_E], dim=1)) m_Es += [m_E] else: out = self.fusion_out[fusion_index](torch.cat([out, f_E], dim=1)) skip = self.fusion_skip[fusion_index](torch.cat([skip, f_E], dim=1)) # remove the noise input batch, _, height, width = out.shape noise = x.new_empty(batch, 1, height * 2, width * 2).normal_().detach() * 0.0 out = conv1(out, adastyles[:, _index+6], noise=noise) out = conv2(out, adastyles[:, _index+7], noise=noise) skip = to_rgb(out, adastyles[:, _index+8], skip) _index += 2 image = skip if return_mask and self.backbone == 'dualstylegan': return image, m_Es return image def stylegan(self): if self.backbone == 'dualstylegan': return self.generator.generator else: return self.generator def zplus2wplus(self, zplus): return self.stylegan().style(zplus.reshape(zplus.shape[0]*zplus.shape[1], zplus.shape[2])).reshape(zplus.shape)