import math
import random
import torch
from torch import nn
from torch.nn import functional as F
class NormStyleCode(nn.Module):
def forward(self, x):
"""Normalize the style codes.
Args:
x (Tensor): Style codes with shape (b, c).
Returns:
Tensor: Normalized tensor.
"""
return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
class ModulatedConv2d(nn.Module):
"""Modulated Conv2d used in StyleGAN2.
There is no bias in ModulatedConv2d.
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.
num_style_feat (int): Channel number of style features.
demodulate (bool): Whether to demodulate in the conv layer. Default: True.
sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
eps (float): A value added to the denominator for numerical stability. Default: 1e-8.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
num_style_feat,
demodulate=True,
sample_mode=None,
eps=1e-8):
super(ModulatedConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.demodulate = demodulate
self.sample_mode = sample_mode
self.eps = eps
# modulation inside each modulated conv
self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
# initialization
# default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
self.weight = nn.Parameter(
torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
math.sqrt(in_channels * kernel_size**2))
self.padding = kernel_size // 2
def forward(self, x, style):
"""Forward function.
Args:
x (Tensor): Tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
Returns:
Tensor: Modulated tensor after convolution.
"""
b, c, h, w = x.shape # c = c_in
# weight modulation
style = self.modulation(style).view(b, 1, c, 1, 1)
# self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
weight = self.weight * style # (b, c_out, c_in, k, k)
if self.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
# upsample or downsample if necessary
if self.sample_mode == 'upsample':
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
elif self.sample_mode == 'downsample':
x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
b, c, h, w = x.shape
x = x.view(1, b * c, h, w)
# weight: (b*c_out, c_in, k, k), groups=b
out = F.conv2d(x, weight, padding=self.padding, groups=b)
out = out.view(b, self.out_channels, *out.shape[2:4])
return out
def __repr__(self):
return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, '
f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})')
class StyleConv(nn.Module):
"""Style conv used in StyleGAN2.
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.
num_style_feat (int): Channel number of style features.
demodulate (bool): Whether demodulate in the conv layer. Default: True.
sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
"""
def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
super(StyleConv, self).__init__()
self.modulated_conv = ModulatedConv2d(
in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
self.weight = nn.Parameter(torch.zeros(1)) # for noise injection
self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x, style, noise=None):
# modulate
out = self.modulated_conv(x, style) * 2**0.5 # for conversion
# noise injection
if noise is None:
b, _, h, w = out.shape
noise = out.new_empty(b, 1, h, w).normal_()
out = out + self.weight * noise
# add bias
out = out + self.bias
# activation
out = self.activate(out)
return out
class ToRGB(nn.Module):
"""To RGB (image space) from features.
Args:
in_channels (int): Channel number of input.
num_style_feat (int): Channel number of style features.
upsample (bool): Whether to upsample. Default: True.
"""
def __init__(self, in_channels, num_style_feat, upsample=True):
super(ToRGB, self).__init__()
self.upsample = upsample
self.modulated_conv = ModulatedConv2d(
in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
def forward(self, x, style, skip=None):
"""Forward function.
Args:
x (Tensor): Feature tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
skip (Tensor): Base/skip tensor. Default: None.
Returns:
Tensor: RGB images.
"""
out = self.modulated_conv(x, style)
out = out + self.bias
if skip is not None:
if self.upsample:
skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
out = out + skip
return out
class ConstantInput(nn.Module):
"""Constant input.
Args:
num_channel (int): Channel number of constant input.
size (int): Spatial size of constant input.
"""
def __init__(self, num_channel, size):
super(ConstantInput, self).__init__()
self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
def forward(self, batch):
out = self.weight.repeat(batch, 1, 1, 1)
return out
class StyleGAN2GeneratorClean(nn.Module):
"""Clean version of StyleGAN2 Generator.
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): Narrow ratio for channels. Default: 1.0.
"""
def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1):
super(StyleGAN2GeneratorClean, self).__init__()
# Style MLP layers
self.num_style_feat = num_style_feat
style_mlp_layers = [NormStyleCode()]
for i in range(num_mlp):
style_mlp_layers.extend(
[nn.Linear(num_style_feat, num_style_feat, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True)])
self.style_mlp = nn.Sequential(*style_mlp_layers)
# initialization
# default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu')
# channel list
channels = {
'4': int(512 * narrow),
'8': int(512 * narrow),
'16': int(512 * narrow),
'32': int(512 * narrow),
'64': int(256 * channel_multiplier * narrow),
'128': int(128 * channel_multiplier * narrow),
'256': int(64 * channel_multiplier * narrow),
'512': int(32 * channel_multiplier * narrow),
'1024': int(16 * channel_multiplier * narrow)
}
self.channels = channels
self.constant_input = ConstantInput(channels['4'], size=4)
self.style_conv1 = StyleConv(
channels['4'],
channels['4'],
kernel_size=3,
num_style_feat=num_style_feat,
demodulate=True,
sample_mode=None)
self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False)
self.log_size = int(math.log(out_size, 2))
self.num_layers = (self.log_size - 2) * 2 + 1
self.num_latent = self.log_size * 2 - 2
self.style_convs = nn.ModuleList()
self.to_rgbs = nn.ModuleList()
self.noises = nn.Module()
in_channels = channels['4']
# noise
for layer_idx in range(self.num_layers):
resolution = 2**((layer_idx + 5) // 2)
shape = [1, 1, resolution, resolution]
self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
# style convs and to_rgbs
for i in range(3, self.log_size + 1):
out_channels = channels[f'{2**i}']
self.style_convs.append(
StyleConv(
in_channels,
out_channels,
kernel_size=3,
num_style_feat=num_style_feat,
demodulate=True,
sample_mode='upsample'))
self.style_convs.append(
StyleConv(
out_channels,
out_channels,
kernel_size=3,
num_style_feat=num_style_feat,
demodulate=True,
sample_mode=None))
self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
in_channels = out_channels
def make_noise(self):
"""Make noise for noise injection."""
device = self.constant_input.weight.device
noises = [torch.randn(1, 1, 4, 4, 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 get_latent(self, x):
return self.style_mlp(x)
def mean_latent(self, num_latent):
latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
latent = self.style_mlp(latent_in).mean(0, keepdim=True)
return latent
def forward(self,
styles,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2GeneratorClean.
Args:
styles (list[Tensor]): Sample codes of styles.
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)
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