567 lines
19 KiB
Python
567 lines
19 KiB
Python
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# pylint: skip-file
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# type: ignore
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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 .fused_act import FusedLeakyReLU
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from .stylegan2_arch import (
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ConvLayer,
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EqualConv2d,
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EqualLinear,
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ResBlock,
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ScaledLeakyReLU,
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StyleGAN2Generator,
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)
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class StyleGAN2GeneratorSFT(StyleGAN2Generator):
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"""StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
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Args:
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out_size (int): The spatial size of outputs.
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num_style_feat (int): Channel number of style features. Default: 512.
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num_mlp (int): Layer number of MLP style layers. Default: 8.
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channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
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resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
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applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
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lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
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narrow (float): The narrow ratio for channels. Default: 1.
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sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
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"""
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def __init__(
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self,
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out_size,
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num_style_feat=512,
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num_mlp=8,
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channel_multiplier=2,
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resample_kernel=(1, 3, 3, 1),
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lr_mlp=0.01,
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narrow=1,
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sft_half=False,
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):
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super(StyleGAN2GeneratorSFT, self).__init__(
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out_size,
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num_style_feat=num_style_feat,
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num_mlp=num_mlp,
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channel_multiplier=channel_multiplier,
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resample_kernel=resample_kernel,
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lr_mlp=lr_mlp,
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narrow=narrow,
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)
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self.sft_half = sft_half
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def forward(
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self,
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styles,
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conditions,
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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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truncation=1,
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truncation_latent=None,
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inject_index=None,
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return_latents=False,
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):
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"""Forward function for StyleGAN2GeneratorSFT.
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Args:
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styles (list[Tensor]): Sample codes of styles.
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conditions (list[Tensor]): SFT conditions to generators.
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input_is_latent (bool): Whether input is latent style. Default: False.
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noise (Tensor | None): Input noise or None. Default: None.
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randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
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truncation (float): The truncation ratio. Default: 1.
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truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
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inject_index (int | None): The injection index for mixing noise. Default: None.
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return_latents (bool): Whether to return style latents. Default: False.
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"""
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# style codes -> latents with Style MLP layer
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if not input_is_latent:
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styles = [self.style_mlp(s) for s in styles]
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# noises
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if noise is None:
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if randomize_noise:
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noise = [None] * self.num_layers # for each style conv layer
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else: # use the stored noise
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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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# style truncation
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if truncation < 1:
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style_truncation = []
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for style in styles:
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style_truncation.append(
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truncation_latent + truncation * (style - truncation_latent)
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)
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styles = style_truncation
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# get style latents with injection
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if len(styles) == 1:
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inject_index = self.num_latent
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if styles[0].ndim < 3:
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# repeat latent code for all the layers
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latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
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else: # used for encoder with different latent code for each layer
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latent = styles[0]
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elif len(styles) == 2: # mixing noises
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if inject_index is None:
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inject_index = random.randint(1, self.num_latent - 1)
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latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
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latent2 = (
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styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
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)
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latent = torch.cat([latent1, latent2], 1)
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# main generation
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out = self.constant_input(latent.shape[0])
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out = self.style_conv1(out, latent[:, 0], noise=noise[0])
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skip = self.to_rgb1(out, latent[:, 1])
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i = 1
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for conv1, conv2, noise1, noise2, to_rgb in zip(
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self.style_convs[::2],
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self.style_convs[1::2],
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noise[1::2],
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noise[2::2],
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self.to_rgbs,
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):
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out = conv1(out, latent[:, i], noise=noise1)
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# the conditions may have fewer levels
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if i < len(conditions):
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# SFT part to combine the conditions
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if self.sft_half: # only apply SFT to half of the channels
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out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
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out_sft = out_sft * conditions[i - 1] + conditions[i]
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out = torch.cat([out_same, out_sft], dim=1)
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else: # apply SFT to all the channels
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out = out * conditions[i - 1] + conditions[i]
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out = conv2(out, latent[:, i + 1], noise=noise2)
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skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space
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i += 2
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image = skip
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if return_latents:
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return image, latent
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else:
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return image, None
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class ConvUpLayer(nn.Module):
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"""Convolutional upsampling layer. It uses bilinear upsampler + Conv.
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Args:
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in_channels (int): Channel number of the input.
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out_channels (int): Channel number of the output.
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kernel_size (int): Size of the convolving kernel.
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stride (int): Stride of the convolution. Default: 1
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padding (int): Zero-padding added to both sides of the input. Default: 0.
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bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.
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bias_init_val (float): Bias initialized value. Default: 0.
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activate (bool): Whether use activateion. Default: True.
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"""
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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bias=True,
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bias_init_val=0,
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activate=True,
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):
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super(ConvUpLayer, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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# self.scale is used to scale the convolution weights, which is related to the common initializations.
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self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
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self.weight = nn.Parameter(
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torch.randn(out_channels, in_channels, kernel_size, kernel_size)
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)
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if bias and not activate:
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self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
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else:
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self.register_parameter("bias", None)
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# activation
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if activate:
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if bias:
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self.activation = FusedLeakyReLU(out_channels)
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else:
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self.activation = ScaledLeakyReLU(0.2)
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else:
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self.activation = None
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def forward(self, x):
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# bilinear upsample
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out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False)
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# conv
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out = F.conv2d(
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out,
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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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# activation
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if self.activation is not None:
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out = self.activation(out)
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return out
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class ResUpBlock(nn.Module):
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"""Residual block with upsampling.
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Args:
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in_channels (int): Channel number of the input.
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out_channels (int): Channel number of the output.
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"""
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def __init__(self, in_channels, out_channels):
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super(ResUpBlock, self).__init__()
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self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
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self.conv2 = ConvUpLayer(
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in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True
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)
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self.skip = ConvUpLayer(
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in_channels, out_channels, 1, bias=False, activate=False
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)
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def forward(self, x):
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out = self.conv1(x)
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out = self.conv2(out)
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skip = self.skip(x)
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out = (out + skip) / math.sqrt(2)
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return out
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class GFPGANv1(nn.Module):
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"""The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
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Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
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Args:
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out_size (int): The spatial size of outputs.
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num_style_feat (int): Channel number of style features. Default: 512.
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channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
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resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
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applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
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decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
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fix_decoder (bool): Whether to fix the decoder. Default: True.
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num_mlp (int): Layer number of MLP style layers. Default: 8.
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lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
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input_is_latent (bool): Whether input is latent style. Default: False.
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different_w (bool): Whether to use different latent w for different layers. Default: False.
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narrow (float): The narrow ratio for channels. Default: 1.
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sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
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"""
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def __init__(
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self,
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out_size,
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num_style_feat=512,
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channel_multiplier=1,
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resample_kernel=(1, 3, 3, 1),
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decoder_load_path=None,
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fix_decoder=True,
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# for stylegan decoder
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num_mlp=8,
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lr_mlp=0.01,
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input_is_latent=False,
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different_w=False,
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narrow=1,
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sft_half=False,
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):
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super(GFPGANv1, self).__init__()
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self.input_is_latent = input_is_latent
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self.different_w = different_w
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self.num_style_feat = num_style_feat
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unet_narrow = narrow * 0.5 # by default, use a half of input channels
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channels = {
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"4": int(512 * unet_narrow),
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"8": int(512 * unet_narrow),
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"16": int(512 * unet_narrow),
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"32": int(512 * unet_narrow),
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"64": int(256 * channel_multiplier * unet_narrow),
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"128": int(128 * channel_multiplier * unet_narrow),
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"256": int(64 * channel_multiplier * unet_narrow),
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"512": int(32 * channel_multiplier * unet_narrow),
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"1024": int(16 * channel_multiplier * unet_narrow),
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}
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self.log_size = int(math.log(out_size, 2))
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first_out_size = 2 ** (int(math.log(out_size, 2)))
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self.conv_body_first = ConvLayer(
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3, channels[f"{first_out_size}"], 1, bias=True, activate=True
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)
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# downsample
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in_channels = channels[f"{first_out_size}"]
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self.conv_body_down = nn.ModuleList()
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for i in range(self.log_size, 2, -1):
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out_channels = channels[f"{2**(i - 1)}"]
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self.conv_body_down.append(
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ResBlock(in_channels, out_channels, resample_kernel)
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)
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in_channels = out_channels
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self.final_conv = ConvLayer(
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in_channels, channels["4"], 3, bias=True, activate=True
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)
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# upsample
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in_channels = channels["4"]
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self.conv_body_up = nn.ModuleList()
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for i in range(3, self.log_size + 1):
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out_channels = channels[f"{2**i}"]
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self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
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in_channels = out_channels
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# to RGB
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self.toRGB = nn.ModuleList()
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for i in range(3, self.log_size + 1):
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self.toRGB.append(
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EqualConv2d(
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channels[f"{2**i}"],
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3,
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1,
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stride=1,
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padding=0,
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bias=True,
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bias_init_val=0,
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)
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)
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if different_w:
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linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
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else:
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linear_out_channel = num_style_feat
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self.final_linear = EqualLinear(
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channels["4"] * 4 * 4,
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linear_out_channel,
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bias=True,
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bias_init_val=0,
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lr_mul=1,
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activation=None,
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)
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# the decoder: stylegan2 generator with SFT modulations
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self.stylegan_decoder = StyleGAN2GeneratorSFT(
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|
|
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
|