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import math |
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import random |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d |
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from model_encoder import StyleEncoder |
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class PixelNorm(nn.Module): |
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def __init__(self): |
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super().__init__() |
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def forward(self, input): |
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return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) |
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def make_kernel(k): |
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k = torch.tensor(k, dtype=torch.float32) |
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if k.ndim == 1: |
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k = k[None, :] * k[:, None] |
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k /= k.sum() |
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return k |
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class Upsample(nn.Module): |
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def __init__(self, kernel, factor=2): |
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super().__init__() |
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self.factor = factor |
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kernel = make_kernel(kernel) * (factor ** 2) |
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self.register_buffer('kernel', kernel) |
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p = kernel.shape[0] - factor |
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pad0 = (p + 1) // 2 + factor - 1 |
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pad1 = p // 2 |
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self.pad = (pad0, pad1) |
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def forward(self, input): |
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out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) |
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return out |
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class Downsample(nn.Module): |
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def __init__(self, kernel, factor=2): |
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super().__init__() |
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self.factor = factor |
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kernel = make_kernel(kernel) |
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self.register_buffer('kernel', kernel) |
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p = kernel.shape[0] - factor |
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pad0 = (p + 1) // 2 |
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pad1 = p // 2 |
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self.pad = (pad0, pad1) |
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def forward(self, input): |
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out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) |
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return out |
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class Blur(nn.Module): |
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def __init__(self, kernel, pad, upsample_factor=1): |
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super().__init__() |
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kernel = make_kernel(kernel) |
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if upsample_factor > 1: |
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kernel = kernel * (upsample_factor ** 2) |
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self.register_buffer('kernel', kernel) |
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self.pad = pad |
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def forward(self, input): |
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out = upfirdn2d(input, self.kernel, pad=self.pad) |
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return out |
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class EqualConv2d(nn.Module): |
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def __init__( |
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self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True |
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): |
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super().__init__() |
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self.weight = nn.Parameter( |
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torch.randn(out_channel, in_channel, kernel_size, kernel_size) |
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) |
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self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) |
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self.stride = stride |
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self.padding = padding |
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if bias: |
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self.bias = nn.Parameter(torch.zeros(out_channel)) |
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else: |
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self.bias = None |
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def forward(self, input): |
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out = F.conv2d( |
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input, |
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self.weight * self.scale, |
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bias=self.bias, |
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stride=self.stride, |
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padding=self.padding, |
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) |
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return out |
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def __repr__(self): |
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return ( |
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' |
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f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' |
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) |
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class EqualLinear(nn.Module): |
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def __init__( |
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self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None |
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): |
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super().__init__() |
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) |
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if bias: |
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) |
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else: |
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self.bias = None |
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self.activation = activation |
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul |
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self.lr_mul = lr_mul |
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def forward(self, input): |
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if self.activation: |
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out = F.linear(input, self.weight * self.scale) |
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out = fused_leaky_relu(out, self.bias * self.lr_mul) |
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else: |
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out = F.linear( |
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input, self.weight * self.scale, bias=self.bias * self.lr_mul |
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) |
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return out |
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def __repr__(self): |
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return ( |
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' |
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) |
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class ScaledLeakyReLU(nn.Module): |
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def __init__(self, negative_slope=0.2): |
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super().__init__() |
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self.negative_slope = negative_slope |
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def forward(self, input): |
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out = F.leaky_relu(input, negative_slope=self.negative_slope) |
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return out * math.sqrt(2) |
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class ModulatedConv2d(nn.Module): |
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def __init__( |
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self, |
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in_channel, |
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out_channel, |
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kernel_size, |
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style_dim, |
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demodulate=True, |
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upsample=False, |
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downsample=False, |
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blur_kernel=[1, 3, 3, 1], |
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): |
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super().__init__() |
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self.eps = 1e-8 |
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self.kernel_size = kernel_size |
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self.in_channel = in_channel |
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self.out_channel = out_channel |
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self.upsample = upsample |
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self.downsample = downsample |
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if upsample: |
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factor = 2 |
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p = (len(blur_kernel) - factor) - (kernel_size - 1) |
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pad0 = (p + 1) // 2 + factor - 1 |
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pad1 = p // 2 + 1 |
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self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) |
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if downsample: |
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factor = 2 |
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p = (len(blur_kernel) - factor) + (kernel_size - 1) |
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pad0 = (p + 1) // 2 |
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pad1 = p // 2 |
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self.blur = Blur(blur_kernel, pad=(pad0, pad1)) |
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fan_in = in_channel * kernel_size ** 2 |
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self.scale = 1 / math.sqrt(fan_in) |
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self.padding = kernel_size // 2 |
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self.weight = nn.Parameter( |
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torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) |
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) |
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self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) |
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self.demodulate = demodulate |
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def __repr__(self): |
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return ( |
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f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' |
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f'upsample={self.upsample}, downsample={self.downsample})' |
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) |
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def forward(self, input, style): |
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batch, in_channel, height, width = input.shape |
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style = self.modulation(style).view(batch, 1, in_channel, 1, 1) |
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weight = self.scale * self.weight * style |
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if self.demodulate: |
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demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) |
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weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) |
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weight = weight.view( |
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batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size |
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) |
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if self.upsample: |
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input = input.view(1, batch * in_channel, height, width) |
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weight = weight.view( |
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batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size |
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) |
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weight = weight.transpose(1, 2).reshape( |
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batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size |
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) |
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out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) |
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_, _, height, width = out.shape |
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out = out.view(batch, self.out_channel, height, width) |
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out = self.blur(out) |
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elif self.downsample: |
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input = self.blur(input) |
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_, _, height, width = input.shape |
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input = input.view(1, batch * in_channel, height, width) |
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out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) |
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_, _, height, width = out.shape |
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out = out.view(batch, self.out_channel, height, width) |
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else: |
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input = input.view(1, batch * in_channel, height, width) |
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out = F.conv2d(input, weight, padding=self.padding, groups=batch) |
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_, _, height, width = out.shape |
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out = out.view(batch, self.out_channel, height, width) |
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return out |
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class NoiseInjection(nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.weight = nn.Parameter(torch.zeros(1)) |
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def forward(self, image, noise=None): |
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if noise is None: |
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batch, _, height, width = image.shape |
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noise = image.new_empty(batch, 1, height, width).normal_() |
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return image + self.weight * noise |
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class ConstantInput(nn.Module): |
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def __init__(self, channel, size=4): |
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super().__init__() |
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self.input = nn.Parameter(torch.randn(1, channel, size, size)) |
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def forward(self, input): |
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batch = input.shape[0] |
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out = self.input.repeat(batch, 1, 1, 1) |
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return out |
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class StyledConv(nn.Module): |
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def __init__( |
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self, |
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in_channel, |
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out_channel, |
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kernel_size, |
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style_dim, |
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upsample=False, |
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blur_kernel=[1, 3, 3, 1], |
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demodulate=True, |
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): |
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super().__init__() |
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self.conv = ModulatedConv2d( |
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in_channel, |
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out_channel, |
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kernel_size, |
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style_dim, |
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upsample=upsample, |
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blur_kernel=blur_kernel, |
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demodulate=demodulate, |
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) |
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self.noise = NoiseInjection() |
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self.activate = FusedLeakyReLU(out_channel) |
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def forward(self, input, style, noise=None): |
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out = self.conv(input, style) |
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out = self.noise(out, noise=noise) |
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out = self.activate(out) |
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return out |
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class ToRGB(nn.Module): |
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def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]): |
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super().__init__() |
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if upsample: |
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self.upsample = Upsample(blur_kernel) |
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self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) |
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self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) |
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def forward(self, input, style, skip=None): |
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out = self.conv(input, style) |
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out = out + self.bias |
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if skip is not None: |
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skip = self.upsample(skip) |
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out = out + skip |
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return out |
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class Generator(nn.Module): |
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def __init__( |
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self, |
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size, |
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style_dim, |
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n_mlp, |
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channel_multiplier=2, |
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blur_kernel=[1, 3, 3, 1], |
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lr_mlp=0.01, |
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): |
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super().__init__() |
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self.size = size |
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self.style_dim = style_dim |
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self.embedder = StyleEncoder(style_dim=512, n_mlp=4) |
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layers = [PixelNorm()] |
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for i in range(n_mlp): |
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layers.append( |
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EqualLinear( |
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style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' |
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) |
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) |
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self.embedding = nn.Sequential(*layers) |
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layers = [PixelNorm()] |
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for i in range(n_mlp): |
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layers.append( |
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EqualLinear( |
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style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' |
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) |
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) |
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self.style = nn.Sequential(*layers) |
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self.channels = { |
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4: 512, |
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8: 512, |
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16: 512, |
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32: 512, |
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64: 256 * channel_multiplier, |
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128: 128 * channel_multiplier, |
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256: 64 * channel_multiplier, |
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512: 32 * channel_multiplier, |
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1024: 16 * channel_multiplier, |
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} |
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self.input = ConstantInput(self.channels[4]) |
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self.conv1 = StyledConv( |
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self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel |
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) |
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self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) |
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self.log_size = int(math.log(size, 2)) |
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self.num_layers = (self.log_size - 2) * 2 + 1 |
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self.convs = nn.ModuleList() |
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self.upsamples = nn.ModuleList() |
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self.to_rgbs = nn.ModuleList() |
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self.noises = nn.Module() |
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in_channel = self.channels[4] |
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for layer_idx in range(self.num_layers): |
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res = (layer_idx + 5) // 2 |
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shape = [1, 1, 2 ** res, 2 ** res] |
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self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) |
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for i in range(3, self.log_size + 1): |
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out_channel = self.channels[2 ** i] |
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self.convs.append( |
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StyledConv( |
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in_channel, |
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out_channel, |
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3, |
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style_dim, |
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upsample=True, |
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blur_kernel=blur_kernel, |
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) |
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) |
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self.convs.append( |
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StyledConv( |
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out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel |
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) |
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) |
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self.to_rgbs.append(ToRGB(out_channel, style_dim)) |
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in_channel = out_channel |
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self.n_latent = self.log_size * 2 - 2 |
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self.add_weight = nn.Parameter(torch.ones(1, self.n_latent, 1)) |
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def make_noise(self): |
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device = self.input.input.device |
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noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] |
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for i in range(3, self.log_size + 1): |
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for _ in range(2): |
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noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) |
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return noises |
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def mean_latent(self, n_latent): |
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latent_in = torch.randn( |
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n_latent, self.style_dim, device=self.input.input.device |
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) |
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latent = self.style(latent_in).mean(0, keepdim=True) |
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return latent |
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def get_latent(self, input): |
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return self.style(input) |
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def get_z_embed(self, image): |
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self.embedder.eval() |
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with torch.no_grad(): |
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z_embed = self.embedder(image) |
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return z_embed |
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|
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def forward( |
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self, |
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styles=None, |
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return_latents=False, |
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inject_index=None, |
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truncation=1, |
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truncation_latent=None, |
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input_is_latent=False, |
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noise=None, |
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randomize_noise=True, |
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style_image=None, |
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z_embed=None, |
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only_return_z_embed=False, |
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add_weight_index=None, |
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): |
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if only_return_z_embed and style_image is not None: |
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return self.get_z_embed(style_image) |
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|
|
if not input_is_latent: |
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styles = [self.style(s) for s in styles] |
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|
|
if noise is None: |
|
if randomize_noise: |
|
noise = [None] * self.num_layers |
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else: |
|
noise = [ |
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getattr(self.noises, f'noise_{i}') for i in range(self.num_layers) |
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] |
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|
|
if truncation < 1: |
|
style_t = [] |
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|
|
for style in styles: |
|
style_t.append( |
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truncation_latent + truncation * (style - truncation_latent) |
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) |
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|
|
styles = style_t |
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|
|
if len(styles) < 2: |
|
inject_index = self.n_latent |
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|
|
if styles[0].ndim < 3: |
|
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) |
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|
|
else: |
|
latent = styles[0] |
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|
|
else: |
|
if inject_index is None: |
|
inject_index = random.randint(1, self.n_latent - 1) |
|
|
|
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) |
|
latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) |
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|
|
latent = torch.cat([latent, latent2], 1) |
|
|
|
if z_embed is not None: |
|
latent_style = self.embedding(z_embed) |
|
latent_style = latent_style.unsqueeze(1).repeat(1, self.n_latent, 1) |
|
elif style_image is not None: |
|
z_embed = self.get_z_embed(style_image) |
|
latent_style = self.embedding(z_embed) |
|
latent_style = latent_style.unsqueeze(1).repeat(1, self.n_latent, 1) |
|
else: |
|
latent_style = None |
|
|
|
if latent_style is not None: |
|
self.add_weight.data = self.add_weight.data.clamp(0.0, 1.0) |
|
if add_weight_index is not None: |
|
add_weight_new = self.add_weight.clone() |
|
add_weight_new[:, :add_weight_index, :] = 1.0 |
|
latent = latent * add_weight_new + latent_style * (1 - add_weight_new) |
|
else: |
|
latent = latent * self.add_weight + latent_style * (1 - self.add_weight) |
|
|
|
out = self.input(latent) |
|
out = self.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( |
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self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs |
|
): |
|
out = conv1(out, latent[:, i], noise=noise1) |
|
out = conv2(out, latent[:, i + 1], noise=noise2) |
|
skip = to_rgb(out, latent[:, i + 2], skip) |
|
|
|
i += 2 |
|
|
|
image = skip |
|
|
|
if return_latents: |
|
if style_image is not None or z_embed is not None: |
|
return image, latent, z_embed |
|
else: |
|
return image, latent |
|
|
|
else: |
|
return image, None |
|
|
|
|
|
class ConvLayer(nn.Sequential): |
|
def __init__( |
|
self, |
|
in_channel, |
|
out_channel, |
|
kernel_size, |
|
downsample=False, |
|
blur_kernel=[1, 3, 3, 1], |
|
bias=True, |
|
activate=True, |
|
): |
|
layers = [] |
|
|
|
if downsample: |
|
factor = 2 |
|
p = (len(blur_kernel) - factor) + (kernel_size - 1) |
|
pad0 = (p + 1) // 2 |
|
pad1 = p // 2 |
|
|
|
layers.append(Blur(blur_kernel, pad=(pad0, pad1))) |
|
|
|
stride = 2 |
|
self.padding = 0 |
|
|
|
else: |
|
stride = 1 |
|
self.padding = kernel_size // 2 |
|
|
|
layers.append( |
|
EqualConv2d( |
|
in_channel, |
|
out_channel, |
|
kernel_size, |
|
padding=self.padding, |
|
stride=stride, |
|
bias=bias and not activate, |
|
) |
|
) |
|
|
|
if activate: |
|
if bias: |
|
layers.append(FusedLeakyReLU(out_channel)) |
|
|
|
else: |
|
layers.append(ScaledLeakyReLU(0.2)) |
|
|
|
super().__init__(*layers) |
|
|
|
|
|
class ResBlock(nn.Module): |
|
def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): |
|
super().__init__() |
|
|
|
self.conv1 = ConvLayer(in_channel, in_channel, 3) |
|
self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) |
|
|
|
self.skip = ConvLayer( |
|
in_channel, out_channel, 1, downsample=True, activate=False, bias=False |
|
) |
|
|
|
def forward(self, input): |
|
out = self.conv1(input) |
|
out = self.conv2(out) |
|
|
|
skip = self.skip(input) |
|
out = (out + skip) / math.sqrt(2) |
|
|
|
return out |
|
|
|
|
|
class Discriminator(nn.Module): |
|
def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): |
|
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.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], 1), |
|
) |
|
|
|
def forward(self, input): |
|
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) |
|
out = self.final_linear(out) |
|
|
|
return out |
|
|
|
|
|
class ProjectionDiscriminator(nn.Module): |
|
def __init__(self, size, style_dim=512, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): |
|
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.final_conv = ConvLayer(in_channel + 1, channels[4], 3) |
|
|
|
self.l_out = EqualLinear(in_channel, 1) |
|
self.l_style = EqualLinear(style_dim, in_channel) |
|
|
|
def forward(self, input, style): |
|
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) |
|
|
|
h = torch.sum(out, dim=(2, 3)) |
|
output = self.l_out(h) |
|
output += torch.sum(self.l_style(style) * h, dim=1, keepdim=True) |
|
|
|
return output |
|
|
