GFPGAN / gfpgan /archs /gfpganv1_arch.py
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import math
import random
import torch
from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU,
StyleGAN2Generator)
from basicsr.ops.fused_act import FusedLeakyReLU
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn
from torch.nn import functional as F
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
@ARCH_REGISTRY.register()
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):
"""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
@ARCH_REGISTRY.register()
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):
"""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