323 lines
13 KiB
Python
323 lines
13 KiB
Python
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 .stylegan2_clean_arch import StyleGAN2GeneratorClean
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class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
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"""StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
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It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
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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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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__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False):
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super(StyleGAN2GeneratorCSFT, 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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narrow=narrow)
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self.sft_half = sft_half
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def forward(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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"""Forward function for StyleGAN2GeneratorCSFT.
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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 = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
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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(truncation_latent + truncation * (style - truncation_latent))
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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 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
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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(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
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noise[2::2], self.to_rgbs):
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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 ResBlock(nn.Module):
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"""Residual block with bilinear upsampling/downsampling.
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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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mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
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"""
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def __init__(self, in_channels, out_channels, mode='down'):
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super(ResBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
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self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
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self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
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if mode == 'down':
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self.scale_factor = 0.5
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elif mode == 'up':
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self.scale_factor = 2
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def forward(self, x):
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out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
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# upsample/downsample
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out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
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out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
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# skip
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x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
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skip = self.skip(x)
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out = out + skip
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return out
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class GFPGANv1Clean(nn.Module):
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"""The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
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It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
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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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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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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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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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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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super(GFPGANv1Clean, 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 = nn.Conv2d(3, channels[f'{first_out_size}'], 1)
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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(ResBlock(in_channels, out_channels, mode='down'))
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in_channels = out_channels
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self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1)
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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(ResBlock(in_channels, out_channels, mode='up'))
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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(nn.Conv2d(channels[f'{2**i}'], 3, 1))
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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 = nn.Linear(channels['4'] * 4 * 4, linear_out_channel)
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# the decoder: stylegan2 generator with SFT modulations
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self.stylegan_decoder = StyleGAN2GeneratorCSFT(
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out_size=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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narrow=narrow,
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sft_half=sft_half)
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# load pre-trained stylegan2 model if necessary
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if decoder_load_path:
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self.stylegan_decoder.load_state_dict(
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torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
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# fix decoder without updating params
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if fix_decoder:
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for _, param in self.stylegan_decoder.named_parameters():
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param.requires_grad = False
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# for SFT modulations (scale and shift)
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self.condition_scale = nn.ModuleList()
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self.condition_shift = 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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if sft_half:
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sft_out_channels = out_channels
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else:
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sft_out_channels = out_channels * 2
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self.condition_scale.append(
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nn.Sequential(
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nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True),
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nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1)))
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self.condition_shift.append(
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nn.Sequential(
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nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True),
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nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1)))
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def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs):
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"""Forward function for GFPGANv1Clean.
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Args:
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x (Tensor): Input images.
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return_latents (bool): Whether to return style latents. Default: False.
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return_rgb (bool): Whether return intermediate rgb images. Default: True.
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randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
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"""
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conditions = []
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unet_skips = []
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out_rgbs = []
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# encoder
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feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
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for i in range(self.log_size - 2):
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feat = self.conv_body_down[i](feat)
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unet_skips.insert(0, feat)
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feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
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# style code
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style_code = self.final_linear(feat.view(feat.size(0), -1))
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if self.different_w:
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style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
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# decode
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for i in range(self.log_size - 2):
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# add unet skip
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feat = feat + unet_skips[i]
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# ResUpLayer
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feat = self.conv_body_up[i](feat)
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# generate scale and shift for SFT layers
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scale = self.condition_scale[i](feat)
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conditions.append(scale.clone())
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shift = self.condition_shift[i](feat)
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conditions.append(shift.clone())
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# generate rgb images
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if return_rgb:
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out_rgbs.append(self.toRGB[i](feat))
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# decoder
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image, _ = self.stylegan_decoder([style_code],
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conditions,
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return_latents=return_latents,
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input_is_latent=self.input_is_latent,
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randomize_noise=randomize_noise)
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return image, out_rgbs
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