import math
import random
import functools
import operator
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from op import conv2d_gradfix
if torch.cuda.is_available():
from op.fused_act import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn2d import upfirdn2d
else:
from op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn2d_cpu import upfirdn2d
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
class Upsample(nn.Module):
def __init__(self, kernel, factor=2):
super().__init__()
self.factor = factor
kernel = make_kernel(kernel) * (factor ** 2)
self.register_buffer("kernel", kernel)
p = kernel.shape[0] - factor
pad0 = (p + 1) // 2 + factor - 1
pad1 = p // 2
self.pad = (pad0, pad1)
def forward(self, input):
out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
return out
class Downsample(nn.Module):
def __init__(self, kernel, factor=2):
super().__init__()
self.factor = factor
kernel = make_kernel(kernel)
self.register_buffer("kernel", kernel)
p = kernel.shape[0] - factor
pad0 = (p + 1) // 2
pad1 = p // 2
self.pad = (pad0, pad1)
def forward(self, input):
out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
return out
class Blur(nn.Module):
def __init__(self, kernel, pad, upsample_factor=1):
super().__init__()
kernel = make_kernel(kernel)
if upsample_factor > 1:
kernel = kernel * (upsample_factor ** 2)
self.register_buffer("kernel", kernel)
self.pad = pad
def forward(self, input):
out = upfirdn2d(input, self.kernel, pad=self.pad)
return out
class EqualConv2d(nn.Module):
def __init__(
self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
):
super().__init__()
self.weight = nn.Parameter(
torch.randn(out_channel, in_channel, kernel_size, kernel_size)
)
self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
self.stride = stride
self.padding = padding
if bias:
self.bias = nn.Parameter(torch.zeros(out_channel))
else:
self.bias = None
def forward(self, input):
out = conv2d_gradfix.conv2d(
input,
self.weight * self.scale,
bias=self.bias,
stride=self.stride,
padding=self.padding,
)
return out
def __repr__(self):
return (
f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"
)
class EqualLinear(nn.Module):
def __init__(
self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
if bias:
self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
else:
self.bias = None
self.activation = activation
self.scale = (1 / math.sqrt(in_dim)) * lr_mul
self.lr_mul = lr_mul
def forward(self, input):
if self.activation:
out = F.linear(input, self.weight * self.scale)
out = fused_leaky_relu(out, self.bias * self.lr_mul)
else:
out = F.linear(
input, self.weight * self.scale, bias=self.bias * self.lr_mul
)
return out
def __repr__(self):
return (
f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
)
class ModulatedConv2d(nn.Module):
def __init__(
self,
in_channel,
out_channel,
kernel_size,
style_dim,
demodulate=True,
upsample=False,
downsample=False,
blur_kernel=[1, 3, 3, 1],
fused=True,
):
super().__init__()
self.eps = 1e-8
self.kernel_size = kernel_size
self.in_channel = in_channel
self.out_channel = out_channel
self.upsample = upsample
self.downsample = downsample
if upsample:
factor = 2
p = (len(blur_kernel) - factor) - (kernel_size - 1)
pad0 = (p + 1) // 2 + factor - 1
pad1 = p // 2 + 1
self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
if downsample:
factor = 2
p = (len(blur_kernel) - factor) + (kernel_size - 1)
pad0 = (p + 1) // 2
pad1 = p // 2
self.blur = Blur(blur_kernel, pad=(pad0, pad1))
fan_in = in_channel * kernel_size ** 2
self.scale = 1 / math.sqrt(fan_in)
self.padding = kernel_size // 2
self.weight = nn.Parameter(
torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
)
self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
self.demodulate = demodulate
self.fused = fused
def __repr__(self):
return (
f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
f"upsample={self.upsample}, downsample={self.downsample})"
)
def forward(self, input, style, is_s_code=False):
batch, in_channel, height, width = input.shape
if not self.fused:
weight = self.scale * self.weight.squeeze(0)
if is_s_code:
style = style[self.modulation]
else:
style = self.modulation(style)
if self.demodulate:
w = weight.unsqueeze(0) * style.view(batch, 1, in_channel, 1, 1)
dcoefs = (w.square().sum((2, 3, 4)) + 1e-8).rsqrt()
input = input * style.reshape(batch, in_channel, 1, 1)
if self.upsample:
weight = weight.transpose(0, 1)
out = conv2d_gradfix.conv_transpose2d(
input, weight, padding=0, stride=2
)
out = self.blur(out)
elif self.downsample:
input = self.blur(input)
out = conv2d_gradfix.conv2d(input, weight, padding=0, stride=2)
else:
out = conv2d_gradfix.conv2d(input, weight, padding=self.padding)
if self.demodulate:
out = out * dcoefs.view(batch, -1, 1, 1)
return out
if is_s_code:
style = style[self.modulation]
else:
style = self.modulation(style)
style = style.view(batch, 1, in_channel, 1, 1)
weight = self.scale * self.weight * style
if self.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
weight = weight.view(
batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
)
if self.upsample:
input = input.view(1, batch * in_channel, height, width)
weight = weight.view(
batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
)
weight = weight.transpose(1, 2).reshape(
batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
)
out = conv2d_gradfix.conv_transpose2d(
input, weight, padding=0, stride=2, groups=batch
)
_, _, height, width = out.shape
out = out.view(batch, self.out_channel, height, width)
out = self.blur(out)
elif self.downsample:
input = self.blur(input)
_, _, height, width = input.shape
input = input.view(1, batch * in_channel, height, width)
out = conv2d_gradfix.conv2d(
input, weight, padding=0, stride=2, groups=batch
)
_, _, height, width = out.shape
out = out.view(batch, self.out_channel, height, width)
else:
input = input.view(1, batch * in_channel, height, width)
out = conv2d_gradfix.conv2d(
input, weight, padding=self.padding, groups=batch
)
_, _, height, width = out.shape
out = out.view(batch, self.out_channel, height, width)
return out
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, noise=None):
if noise is None:
batch, _, height, width = image.shape
noise = image.new_empty(batch, 1, height, width).normal_()
return image + self.weight * noise
class ConstantInput(nn.Module):
def __init__(self, channel, size=4):
super().__init__()
self.input = nn.Parameter(torch.randn(1, channel, size, size))
def forward(self, input, is_s_code=False):
if not is_s_code:
batch = input.shape[0]
else:
batch = next(iter(input.values())).shape[0]
out = self.input.repeat(batch, 1, 1, 1)
return out
class StyledConv(nn.Module):
def __init__(
self,
in_channel,
out_channel,
kernel_size,
style_dim,
upsample=False,
blur_kernel=[1, 3, 3, 1],
demodulate=True,
):
super().__init__()
self.conv = ModulatedConv2d(
in_channel,
out_channel,
kernel_size,
style_dim,
upsample=upsample,
blur_kernel=blur_kernel,
demodulate=demodulate,
)
self.noise = NoiseInjection()
# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
# self.activate = ScaledLeakyReLU(0.2)
self.activate = FusedLeakyReLU(out_channel)
def forward(self, input, style, noise=None, is_s_code=False):
out = self.conv(input, style, is_s_code=is_s_code)
out = self.noise(out, noise=noise)
# out = out + self.bias
out = self.activate(out)
return out
class ToRGB(nn.Module):
def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
super().__init__()
if upsample:
self.upsample = Upsample(blur_kernel)
self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
def forward(self, input, style, skip=None, is_s_code=False):
out = self.conv(input, style, is_s_code=is_s_code)
out = out + self.bias
if skip is not None:
skip = self.upsample(skip)
out = out + skip
return out
class Generator(nn.Module):
def __init__(
self,
size,
style_dim,
n_mlp,
channel_multiplier=2,
blur_kernel=[1, 3, 3, 1],
lr_mlp=0.01,
):
super().__init__()
self.size = size
self.style_dim = style_dim
layers = [PixelNorm()]
for i in range(n_mlp):
layers.append(
EqualLinear(
style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
)
)
self.style = nn.Sequential(*layers)
self.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,
}
self.input = ConstantInput(self.channels[4])
self.conv1 = StyledConv(
self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
)
self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
self.log_size = int(math.log(size, 2))
self.num_layers = (self.log_size - 2) * 2 + 1
self.convs = nn.ModuleList()
self.upsamples = nn.ModuleList()
self.to_rgbs = nn.ModuleList()
self.noises = nn.Module()
in_channel = self.channels[4]
for layer_idx in range(self.num_layers):
res = (layer_idx + 5) // 2
shape = [1, 1, 2 ** res, 2 ** res]
self.noises.register_buffer(f"noise_{layer_idx}", torch.randn(*shape))
for i in range(3, self.log_size + 1):
out_channel = self.channels[2 ** i]
self.convs.append(
StyledConv(
in_channel,
out_channel,
3,
style_dim,
upsample=True,
blur_kernel=blur_kernel,
)
)
self.convs.append(
StyledConv(
out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
)
)
self.to_rgbs.append(ToRGB(out_channel, style_dim))
in_channel = out_channel
self.n_latent = self.log_size * 2 - 2
self.modulation_layers = [self.conv1.conv.modulation, self.to_rgb1.conv.modulation] + \
[layer.conv.modulation for layer in self.convs] + \
[layer.conv.modulation for layer in self.to_rgbs]
def make_noise(self):
device = self.input.input.device
noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
for i in range(3, self.log_size + 1):
for _ in range(2):
noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
return noises
def mean_latent(self, n_latent):
latent_in = torch.randn(
n_latent, self.style_dim, device=self.input.input.device
)
latent = self.style(latent_in).mean(0, keepdim=True)
return latent
def get_latent(self, input):
return self.style(input)
def get_s_code(self, styles, input_is_latent):
if not input_is_latent:
styles = [self.style(s) for s in styles]
s_codes = [{# const block
self.modulation_layers[0]: self.modulation_layers[0](style[:, 0]), #s0
self.modulation_layers[1]: self.modulation_layers[1](style[:, 1]), #s1
# conv layers
self.modulation_layers[2]: self.modulation_layers[2](style[:, 1]), #s2
self.modulation_layers[3]: self.modulation_layers[3](style[:, 2]), #s3
self.modulation_layers[4]: self.modulation_layers[4](style[:, 3]), #s5
self.modulation_layers[5]: self.modulation_layers[5](style[:, 4]), #s6
self.modulation_layers[6]: self.modulation_layers[6](style[:, 5]), #s8
self.modulation_layers[7]: self.modulation_layers[7](style[:, 6]), #s9
self.modulation_layers[8]: self.modulation_layers[8](style[:, 7]), #s11
self.modulation_layers[9]: self.modulation_layers[9](style[:, 8]), #s12
self.modulation_layers[10]: self.modulation_layers[10](style[:, 9]), #s14
self.modulation_layers[11]: self.modulation_layers[11](style[:, 10]), #s15
self.modulation_layers[12]: self.modulation_layers[12](style[:, 11]), #s17
self.modulation_layers[13]: self.modulation_layers[13](style[:, 12]), #s18
self.modulation_layers[14]: self.modulation_layers[14](style[:, 13]), #s20
self.modulation_layers[15]: self.modulation_layers[15](style[:, 14]), #s21
self.modulation_layers[16]: self.modulation_layers[16](style[:, 15]), #s23
self.modulation_layers[17]: self.modulation_layers[17](style[:, 16]), #s24
# toRGB layers
self.modulation_layers[18]: self.modulation_layers[18](style[:, 3]), #s4
self.modulation_layers[19]: self.modulation_layers[19](style[:, 5]), #s7
self.modulation_layers[20]: self.modulation_layers[20](style[:, 7]), #s10
self.modulation_layers[21]: self.modulation_layers[21](style[:, 9]), #s13
self.modulation_layers[22]: self.modulation_layers[22](style[:, 11]), #s16
self.modulation_layers[23]: self.modulation_layers[23](style[:, 13]), #s19
self.modulation_layers[24]: self.modulation_layers[24](style[:, 15]), #s22
self.modulation_layers[25]: self.modulation_layers[25](style[:, 17]), #s25
} for style in styles]
return s_codes
def forward(
self,
styles,
return_latents=False,
inject_index=None,
truncation=1,
truncation_latent=None,
input_is_latent=False,
input_is_s_code=False,
noise=None,
randomize_noise=True,
):
if not input_is_s_code:
return self.forward_with_w(styles, return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise, randomize_noise)
return self.forward_with_s(styles, return_latents, noise, randomize_noise)
def forward_with_w(
self,
styles,
return_latents=False,
inject_index=None,
truncation=1,
truncation_latent=None,
input_is_latent=False,
noise=None,
randomize_noise=True,
):
if not input_is_latent:
styles = [self.style(s) for s in styles]
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers
else:
noise = [
getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
]
if truncation < 1:
style_t = []
for style in styles:
style_t.append(
truncation_latent + truncation * (style - truncation_latent)
)
styles = style_t
if len(styles) < 2:
inject_index = self.n_latent
if styles[0].ndim < 3:
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else:
latent = styles[0]
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)
latent = torch.cat([latent, latent2], 1)
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(
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:
return image, latent
else:
return image, None
def forward_with_s(
self,
styles,
return_latents=False,
noise=None,
randomize_noise=True,
):
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers
else:
noise = [
getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
]
out = self.input(styles, is_s_code=True)
out = self.conv1(out, styles, is_s_code=True, noise=noise[0])
skip = self.to_rgb1(out, styles, is_s_code=True)
i = 1
for conv1, conv2, noise1, noise2, to_rgb in zip(
self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
):
out = conv1(out, styles, is_s_code=True, noise=noise1)
out = conv2(out, styles, is_s_code=True, noise=noise2)
skip = to_rgb(out, styles, skip, is_s_code=True)
i += 2
image = skip
if return_latents:
return image, styles
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:
layers.append(FusedLeakyReLU(out_channel, bias=bias))
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