diff options
Diffstat (limited to 'r_chainner/archs/face/gfpganv1_clean_arch.py')
| -rw-r--r-- | r_chainner/archs/face/gfpganv1_clean_arch.py | 370 |
1 files changed, 370 insertions, 0 deletions
diff --git a/r_chainner/archs/face/gfpganv1_clean_arch.py b/r_chainner/archs/face/gfpganv1_clean_arch.py new file mode 100644 index 0000000..f3c4d49 --- /dev/null +++ b/r_chainner/archs/face/gfpganv1_clean_arch.py @@ -0,0 +1,370 @@ +# pylint: skip-file
+# type: ignore
+import math
+import random
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from r_chainner.archs.face.stylegan2_clean_arch import StyleGAN2GeneratorClean
+
+
+class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
+ """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
+ It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
+ 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): 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,
+ narrow=1,
+ sft_half=False,
+ ):
+ super(StyleGAN2GeneratorCSFT, self).__init__(
+ out_size,
+ num_style_feat=num_style_feat,
+ num_mlp=num_mlp,
+ channel_multiplier=channel_multiplier,
+ 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 StyleGAN2GeneratorCSFT.
+ 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 ResBlock(nn.Module):
+ """Residual block with bilinear upsampling/downsampling.
+ Args:
+ in_channels (int): Channel number of the input.
+ out_channels (int): Channel number of the output.
+ mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
+ """
+
+ def __init__(self, in_channels, out_channels, mode="down"):
+ super(ResBlock, self).__init__()
+
+ self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
+ self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
+ self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
+ if mode == "down":
+ self.scale_factor = 0.5
+ elif mode == "up":
+ self.scale_factor = 2
+
+ def forward(self, x):
+ out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
+ # upsample/downsample
+ out = F.interpolate(
+ out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
+ )
+ out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
+ # skip
+ x = F.interpolate(
+ x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
+ )
+ skip = self.skip(x)
+ out = out + skip
+ return out
+
+
+class GFPGANv1Clean(nn.Module):
+ """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
+ It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
+ 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.
+ 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.
+ 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,
+ state_dict,
+ ):
+ super(GFPGANv1Clean, self).__init__()
+
+ out_size = 512
+ num_style_feat = 512
+ channel_multiplier = 2
+ decoder_load_path = None
+ fix_decoder = False
+ num_mlp = 8
+ input_is_latent = True
+ different_w = True
+ narrow = 1
+ sft_half = True
+
+ self.model_arch = "GFPGAN"
+ self.sub_type = "Face SR"
+ self.scale = 8
+ self.in_nc = 3
+ self.out_nc = 3
+ self.state = state_dict
+
+ self.supports_fp16 = False
+ self.supports_bf16 = True
+ self.min_size_restriction = 512
+
+ 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 = nn.Conv2d(3, channels[f"{first_out_size}"], 1)
+
+ # 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, mode="down"))
+ in_channels = out_channels
+
+ self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1)
+
+ # 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(ResBlock(in_channels, out_channels, mode="up"))
+ in_channels = out_channels
+
+ # to RGB
+ self.toRGB = nn.ModuleList()
+ for i in range(3, self.log_size + 1):
+ self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1))
+
+ 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 = nn.Linear(channels["4"] * 4 * 4, linear_out_channel)
+
+ # the decoder: stylegan2 generator with SFT modulations
+ self.stylegan_decoder = StyleGAN2GeneratorCSFT(
+ out_size=out_size,
+ num_style_feat=num_style_feat,
+ num_mlp=num_mlp,
+ channel_multiplier=channel_multiplier,
+ 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(
+ nn.Conv2d(out_channels, out_channels, 3, 1, 1),
+ nn.LeakyReLU(0.2, True),
+ nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
+ )
+ )
+ self.condition_shift.append(
+ nn.Sequential(
+ nn.Conv2d(out_channels, out_channels, 3, 1, 1),
+ nn.LeakyReLU(0.2, True),
+ nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
+ )
+ )
+ self.load_state_dict(state_dict)
+
+ def forward(
+ self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs
+ ):
+ """Forward function for GFPGANv1Clean.
+ 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 = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
+ for i in range(self.log_size - 2):
+ feat = self.conv_body_down[i](feat)
+ unet_skips.insert(0, feat)
+ feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
+
+ # 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
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