567 lines
19 KiB
Python
567 lines
19 KiB
Python
# pylint: skip-file
|
|
# type: ignore
|
|
import math
|
|
import random
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
|
|
from .fused_act import FusedLeakyReLU
|
|
from .stylegan2_arch import (
|
|
ConvLayer,
|
|
EqualConv2d,
|
|
EqualLinear,
|
|
ResBlock,
|
|
ScaledLeakyReLU,
|
|
StyleGAN2Generator,
|
|
)
|
|
|
|
|
|
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
|
|
|
|
|
|
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, **kwargs
|
|
):
|
|
"""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
|
|
|
|
|
|
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, **kwargs):
|
|
"""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
|