spaces demo

LICENSE

@@ -0,0 +1,351 @@
+Tencent is pleased to support the open source community by making GFPGAN available.
+
+Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
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+GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
+
+
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+The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
+The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
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+Open Source Software licensed under the MIT license:
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+Please note that only files in cv2 package are used.
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+---------------------------------------------
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+Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
+---------------------------------------------
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+Copyright (c) 2013 noamraph
+
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+MIT License (MIT)
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+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

MANIFEST.in

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+include assets/*
+include inputs/*
+include scripts/*.py
+include inference_gfpgan.py
+include VERSION
+include LICENSE
+include requirements.txt
+include gfpgan/weights/README.md

PaperModel.md

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+# Installation
+
+We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.<br>
+If you want want to use the original model in our paper, please follow the instructions below.
+
+1. Clone repo
+
+    ```bash
+    git clone https://github.com/xinntao/GFPGAN.git
+    cd GFPGAN
+    ```
+
+1. Install dependent packages
+
+    As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**.
+    You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details.
+
+    **Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues)
+
+    ```bash
+    # Install basicsr - https://github.com/xinntao/BasicSR
+    # We use BasicSR for both training and inference
+    pip install basicsr
+
+    # Install facexlib - https://github.com/xinntao/facexlib
+    # We use face detection and face restoration helper in the facexlib package
+    pip install facexlib
+
+    pip install -r requirements.txt
+    python setup.py develop
+
+    # remember to set BASICSR_JIT=True before your running commands
+    ```
+
+    **Option 2: Compile extensions during installation** (For those need to train/inference for many times)
+
+    ```bash
+    # Install basicsr - https://github.com/xinntao/BasicSR
+    # We use BasicSR for both training and inference
+    # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
+    # Add -vvv for detailed log prints
+    BASICSR_EXT=True pip install basicsr -vvv
+
+    # Install facexlib - https://github.com/xinntao/facexlib
+    # We use face detection and face restoration helper in the facexlib package
+    pip install facexlib
+
+    pip install -r requirements.txt
+    python setup.py develop
+    ```
+
+## :zap: Quick Inference
+
+Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
+
+```bash
+wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
+```
+
+- Option 1: Load extensions just-in-time(JIT)
+
+    ```bash
+    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
+
+    # for aligned images
+    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
+    ```
+
+- Option 2: Have successfully compiled extensions during installation
+
+    ```bash
+    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
+
+    # for aligned images
+    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
+    ```

VERSION

@@ -0,0 +1 @@
+0.2.1

experiments/pretrained_models/README.md

@@ -0,0 +1,7 @@
+# Pre-trained Models and Other Data
+
+Download pre-trained models and other data. Put them in this folder.
+
+1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
+1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
+1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)

gfpgan/__init__.py

@@ -0,0 +1,6 @@
+# flake8: noqa
+from .archs import *
+from .data import *
+from .models import *
+from .utils import *
+from .version import __gitsha__, __version__

gfpgan/archs/__init__.py

@@ -0,0 +1,10 @@
+import importlib
+from basicsr.utils import scandir
+from os import path as osp
+
+# automatically scan and import arch modules for registry
+# scan all the files that end with '_arch.py' under the archs folder
+arch_folder = osp.dirname(osp.abspath(__file__))
+arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
+# import all the arch modules
+_arch_modules = [importlib.import_module(f'gfpgan.archs.{file_name}') for file_name in arch_filenames]

gfpgan/archs/arcface_arch.py

@@ -0,0 +1,197 @@
+import torch.nn as nn
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class SEBlock(nn.Module):
+
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
+            nn.Sigmoid())
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+@ARCH_REGISTRY.register()
+class ResNetArcFace(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        if block == 'IRBlock':
+            block = IRBlock
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNetArcFace, self).__init__()
+        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn4 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc5 = nn.Linear(512 * 8 * 8, 512)
+        self.bn5 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.bn4(x)
+        x = self.dropout(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc5(x)
+        x = self.bn5(x)
+
+        return x

gfpgan/archs/gfpganv1_arch.py

@@ -0,0 +1,417 @@
+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.
+
+    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
+            kenrel to 2D resample kernel. Default: [1, 3, 3, 1].
+        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
+    """
+
+    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 StyleGAN2Generator.
+
+        Args:
+            styles (list[Tensor]): Sample codes of styles.
+            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): TODO. Default: 1.
+            truncation_latent (Tensor | None): TODO. 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 latent 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:
+                    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:
+                    out = out * conditions[i - 1] + conditions[i]
+
+            out = conv2(out, latent[:, i + 1], noise=noise2)
+            skip = to_rgb(out, latent[:, i + 2], skip)
+            i += 2
+
+        image = skip
+
+        if return_latents:
+            return image, latent
+        else:
+            return image, None
+
+
+class ConvUpLayer(nn.Module):
+    """Conv Up Layer. Bilinear upsample + 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 = 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):
+    """Unet + StyleGAN2 decoder with SFT."""
+
+    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
+        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)
+
+        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)
+
+        if decoder_load_path:
+            self.stylegan_decoder.load_state_dict(
+                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
+        if fix_decoder:
+            for _, param in self.stylegan_decoder.named_parameters():
+                param.requires_grad = False
+
+        # for SFT
+        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,
+                save_feat_path=None,
+                load_feat_path=None,
+                return_rgb=True,
+                randomize_noise=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 layer
+            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))
+
+        if save_feat_path is not None:
+            torch.save(conditions, save_feat_path)
+        if load_feat_path is not None:
+            conditions = torch.load(load_feat_path)
+            conditions = [v.cuda() for v in conditions]
+
+        # 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):
+
+    def __init__(self):
+        super(FacialComponentDiscriminator, self).__init__()
+
+        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):
+        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

gfpgan/archs/gfpganv1_clean_arch.py

@@ -0,0 +1,304 @@
+import math
+import random
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .stylegan2_clean_arch import StyleGAN2GeneratorClean
+
+
+class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
+    """StyleGAN2 Generator.
+
+    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.
+    """
+
+    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 StyleGAN2Generator.
+
+        Args:
+            styles (list[Tensor]): Sample codes of styles.
+            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): TODO. Default: 1.
+            truncation_latent (Tensor | None): TODO. 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 latent 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:
+                    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:
+                    out = out * conditions[i - 1] + conditions[i]
+
+            out = conv2(out, latent[:, i + 1], noise=noise2)
+            skip = to_rgb(out, latent[:, i + 2], skip)
+            i += 2
+
+        image = skip
+
+        if return_latents:
+            return image, latent
+        else:
+            return image, None
+
+
+class ResBlock(nn.Module):
+    """Residual block with upsampling/downsampling.
+
+    Args:
+        in_channels (int): Channel number of the input.
+        out_channels (int): Channel number of the output.
+    """
+
+    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):
+    """GFPGANv1 Clean version."""
+
+    def __init__(
+            self,
+            out_size,
+            num_style_feat=512,
+            channel_multiplier=1,
+            decoder_load_path=None,
+            fix_decoder=True,
+            # for stylegan decoder
+            num_mlp=8,
+            input_is_latent=False,
+            different_w=False,
+            narrow=1,
+            sft_half=False):
+
+        super(GFPGANv1Clean, 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
+        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)
+
+        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)
+
+        if decoder_load_path:
+            self.stylegan_decoder.load_state_dict(
+                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
+        if fix_decoder:
+            for name, param in self.stylegan_decoder.named_parameters():
+                param.requires_grad = False
+
+        # for SFT
+        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)))
+
+    def forward(self,
+                x,
+                return_latents=False,
+                save_feat_path=None,
+                load_feat_path=None,
+                return_rgb=True,
+                randomize_noise=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 layer
+            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))
+
+        if save_feat_path is not None:
+            torch.save(conditions, save_feat_path)
+        if load_feat_path is not None:
+            conditions = torch.load(load_feat_path)
+            conditions = [v.cuda() for v in conditions]
+
+        # 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

gfpgan/archs/stylegan2_clean_arch.py

@@ -0,0 +1,377 @@
+import math
+import random
+import torch
+from basicsr.archs.arch_util import default_init_weights
+from basicsr.utils.registry import ARCH_REGISTRY
+from torch import nn
+from torch.nn import functional as F
+
+
+class NormStyleCode(nn.Module):
+
+    def forward(self, x):
+        """Normalize the style codes.
+
+        Args:
+            x (Tensor): Style codes with shape (b, c).
+
+        Returns:
+            Tensor: Normalized tensor.
+        """
+        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
+
+
+class ModulatedConv2d(nn.Module):
+    """Modulated Conv2d used in StyleGAN2.
+
+    There is no bias in ModulatedConv2d.
+
+    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.
+        num_style_feat (int): Channel number of style features.
+        demodulate (bool): Whether to demodulate in the conv layer.
+            Default: True.
+        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
+            Default: None.
+        eps (float): A value added to the denominator for numerical stability.
+            Default: 1e-8.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 num_style_feat,
+                 demodulate=True,
+                 sample_mode=None,
+                 eps=1e-8):
+        super(ModulatedConv2d, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.demodulate = demodulate
+        self.sample_mode = sample_mode
+        self.eps = eps
+
+        # modulation inside each modulated conv
+        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
+        # initialization
+        default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
+
+        self.weight = nn.Parameter(
+            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
+            math.sqrt(in_channels * kernel_size**2))
+        self.padding = kernel_size // 2
+
+    def forward(self, x, style):
+        """Forward function.
+
+        Args:
+            x (Tensor): Tensor with shape (b, c, h, w).
+            style (Tensor): Tensor with shape (b, num_style_feat).
+
+        Returns:
+            Tensor: Modulated tensor after convolution.
+        """
+        b, c, h, w = x.shape  # c = c_in
+        # weight modulation
+        style = self.modulation(style).view(b, 1, c, 1, 1)
+        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
+        weight = self.weight * style  # (b, c_out, c_in, k, k)
+
+        if self.demodulate:
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
+            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
+
+        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
+
+        if self.sample_mode == 'upsample':
+            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
+        elif self.sample_mode == 'downsample':
+            x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
+
+        b, c, h, w = x.shape
+        x = x.view(1, b * c, h, w)
+        # weight: (b*c_out, c_in, k, k), groups=b
+        out = F.conv2d(x, weight, padding=self.padding, groups=b)
+        out = out.view(b, self.out_channels, *out.shape[2:4])
+
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
+                f'out_channels={self.out_channels}, '
+                f'kernel_size={self.kernel_size}, '
+                f'demodulate={self.demodulate}, sample_mode={self.sample_mode})')
+
+
+class StyleConv(nn.Module):
+    """Style 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.
+        num_style_feat (int): Channel number of style features.
+        demodulate (bool): Whether demodulate in the conv layer. Default: True.
+        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
+            Default: None.
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
+        super(StyleConv, self).__init__()
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
+        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
+        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
+        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x, style, noise=None):
+        # modulate
+        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
+        # noise injection
+        if noise is None:
+            b, _, h, w = out.shape
+            noise = out.new_empty(b, 1, h, w).normal_()
+        out = out + self.weight * noise
+        # add bias
+        out = out + self.bias
+        # activation
+        out = self.activate(out)
+        return out
+
+
+class ToRGB(nn.Module):
+    """To RGB from features.
+
+    Args:
+        in_channels (int): Channel number of input.
+        num_style_feat (int): Channel number of style features.
+        upsample (bool): Whether to upsample. Default: True.
+    """
+
+    def __init__(self, in_channels, num_style_feat, upsample=True):
+        super(ToRGB, self).__init__()
+        self.upsample = upsample
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, x, style, skip=None):
+        """Forward function.
+
+        Args:
+            x (Tensor): Feature tensor with shape (b, c, h, w).
+            style (Tensor): Tensor with shape (b, num_style_feat).
+            skip (Tensor): Base/skip tensor. Default: None.
+
+        Returns:
+            Tensor: RGB images.
+        """
+        out = self.modulated_conv(x, style)
+        out = out + self.bias
+        if skip is not None:
+            if self.upsample:
+                skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
+            out = out + skip
+        return out
+
+
+class ConstantInput(nn.Module):
+    """Constant input.
+
+    Args:
+        num_channel (int): Channel number of constant input.
+        size (int): Spatial size of constant input.
+    """
+
+    def __init__(self, num_channel, size):
+        super(ConstantInput, self).__init__()
+        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
+
+    def forward(self, batch):
+        out = self.weight.repeat(batch, 1, 1, 1)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class StyleGAN2GeneratorClean(nn.Module):
+    """Clean version of StyleGAN2 Generator.
+
+    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): Narrow ratio for channels. Default: 1.0.
+    """
+
+    def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1):
+        super(StyleGAN2GeneratorClean, self).__init__()
+        # Style MLP layers
+        self.num_style_feat = num_style_feat
+        style_mlp_layers = [NormStyleCode()]
+        for i in range(num_mlp):
+            style_mlp_layers.extend(
+                [nn.Linear(num_style_feat, num_style_feat, bias=True),
+                 nn.LeakyReLU(negative_slope=0.2, inplace=True)])
+        self.style_mlp = nn.Sequential(*style_mlp_layers)
+        # initialization
+        default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu')
+
+        channels = {
+            '4': int(512 * narrow),
+            '8': int(512 * narrow),
+            '16': int(512 * narrow),
+            '32': int(512 * narrow),
+            '64': int(256 * channel_multiplier * narrow),
+            '128': int(128 * channel_multiplier * narrow),
+            '256': int(64 * channel_multiplier * narrow),
+            '512': int(32 * channel_multiplier * narrow),
+            '1024': int(16 * channel_multiplier * narrow)
+        }
+        self.channels = channels
+
+        self.constant_input = ConstantInput(channels['4'], size=4)
+        self.style_conv1 = StyleConv(
+            channels['4'],
+            channels['4'],
+            kernel_size=3,
+            num_style_feat=num_style_feat,
+            demodulate=True,
+            sample_mode=None)
+        self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False)
+
+        self.log_size = int(math.log(out_size, 2))
+        self.num_layers = (self.log_size - 2) * 2 + 1
+        self.num_latent = self.log_size * 2 - 2
+
+        self.style_convs = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+        self.noises = nn.Module()
+
+        in_channels = channels['4']
+        # noise
+        for layer_idx in range(self.num_layers):
+            resolution = 2**((layer_idx + 5) // 2)
+            shape = [1, 1, resolution, resolution]
+            self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
+        # style convs and to_rgbs
+        for i in range(3, self.log_size + 1):
+            out_channels = channels[f'{2**i}']
+            self.style_convs.append(
+                StyleConv(
+                    in_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode='upsample'))
+            self.style_convs.append(
+                StyleConv(
+                    out_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode=None))
+            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
+            in_channels = out_channels
+
+    def make_noise(self):
+        """Make noise for noise injection."""
+        device = self.constant_input.weight.device
+        noises = [torch.randn(1, 1, 4, 4, device=device)]
+
+        for i in range(3, self.log_size + 1):
+            for _ in range(2):
+                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
+
+        return noises
+
+    def get_latent(self, x):
+        return self.style_mlp(x)
+
+    def mean_latent(self, num_latent):
+        latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
+        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
+        return latent
+
+    def forward(self,
+                styles,
+                input_is_latent=False,
+                noise=None,
+                randomize_noise=True,
+                truncation=1,
+                truncation_latent=None,
+                inject_index=None,
+                return_latents=False):
+        """Forward function for StyleGAN2Generator.
+
+        Args:
+            styles (list[Tensor]): Sample codes of styles.
+            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): TODO. Default: 1.
+            truncation_latent (Tensor | None): TODO. 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 latent 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)
+            out = conv2(out, latent[:, i + 1], noise=noise2)
+            skip = to_rgb(out, latent[:, i + 2], skip)
+            i += 2
+
+        image = skip
+
+        if return_latents:
+            return image, latent
+        else:
+            return image, None

gfpgan/data/__init__.py

@@ -0,0 +1,10 @@
+import importlib
+from basicsr.utils import scandir
+from os import path as osp
+
+# automatically scan and import dataset modules for registry
+# scan all the files that end with '_dataset.py' under the data folder
+data_folder = osp.dirname(osp.abspath(__file__))
+dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
+# import all the dataset modules
+_dataset_modules = [importlib.import_module(f'gfpgan.data.{file_name}') for file_name in dataset_filenames]

gfpgan/data/ffhq_degradation_dataset.py

@@ -0,0 +1,212 @@
+import cv2
+import math
+import numpy as np
+import os.path as osp
+import torch
+import torch.utils.data as data
+from basicsr.data import degradations as degradations
+from basicsr.data.data_util import paths_from_folder
+from basicsr.data.transforms import augment
+from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
+from basicsr.utils.registry import DATASET_REGISTRY
+from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation,
+                                               normalize)
+
+
+@DATASET_REGISTRY.register()
+class FFHQDegradationDataset(data.Dataset):
+
+    def __init__(self, opt):
+        super(FFHQDegradationDataset, self).__init__()
+        self.opt = opt
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+
+        self.gt_folder = opt['dataroot_gt']
+        self.mean = opt['mean']
+        self.std = opt['std']
+        self.out_size = opt['out_size']
+
+        self.crop_components = opt.get('crop_components', False)  # facial components
+        self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1)
+
+        if self.crop_components:
+            self.components_list = torch.load(opt.get('component_path'))
+
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.io_backend_opt['db_paths'] = self.gt_folder
+            if not self.gt_folder.endswith('.lmdb'):
+                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
+            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
+                self.paths = [line.split('.')[0] for line in fin]
+        else:
+            self.paths = paths_from_folder(self.gt_folder)
+
+        # degradations
+        self.blur_kernel_size = opt['blur_kernel_size']
+        self.kernel_list = opt['kernel_list']
+        self.kernel_prob = opt['kernel_prob']
+        self.blur_sigma = opt['blur_sigma']
+        self.downsample_range = opt['downsample_range']
+        self.noise_range = opt['noise_range']
+        self.jpeg_range = opt['jpeg_range']
+
+        # color jitter
+        self.color_jitter_prob = opt.get('color_jitter_prob')
+        self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob')
+        self.color_jitter_shift = opt.get('color_jitter_shift', 20)
+        # to gray
+        self.gray_prob = opt.get('gray_prob')
+
+        logger = get_root_logger()
+        logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, '
+                    f'sigma: [{", ".join(map(str, self.blur_sigma))}]')
+        logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]')
+        logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]')
+        logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]')
+
+        if self.color_jitter_prob is not None:
+            logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, '
+                        f'shift: {self.color_jitter_shift}')
+        if self.gray_prob is not None:
+            logger.info(f'Use random gray. Prob: {self.gray_prob}')
+
+        self.color_jitter_shift /= 255.
+
+    @staticmethod
+    def color_jitter(img, shift):
+        jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32)
+        img = img + jitter_val
+        img = np.clip(img, 0, 1)
+        return img
+
+    @staticmethod
+    def color_jitter_pt(img, brightness, contrast, saturation, hue):
+        fn_idx = torch.randperm(4)
+        for fn_id in fn_idx:
+            if fn_id == 0 and brightness is not None:
+                brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
+                img = adjust_brightness(img, brightness_factor)
+
+            if fn_id == 1 and contrast is not None:
+                contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
+                img = adjust_contrast(img, contrast_factor)
+
+            if fn_id == 2 and saturation is not None:
+                saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
+                img = adjust_saturation(img, saturation_factor)
+
+            if fn_id == 3 and hue is not None:
+                hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
+                img = adjust_hue(img, hue_factor)
+        return img
+
+    def get_component_coordinates(self, index, status):
+        components_bbox = self.components_list[f'{index:08d}']
+        if status[0]:  # hflip
+            # exchange right and left eye
+            tmp = components_bbox['left_eye']
+            components_bbox['left_eye'] = components_bbox['right_eye']
+            components_bbox['right_eye'] = tmp
+            # modify the width coordinate
+            components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0]
+            components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0]
+            components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0]
+
+        # get coordinates
+        locations = []
+        for part in ['left_eye', 'right_eye', 'mouth']:
+            mean = components_bbox[part][0:2]
+            half_len = components_bbox[part][2]
+            if 'eye' in part:
+                half_len *= self.eye_enlarge_ratio
+            loc = np.hstack((mean - half_len + 1, mean + half_len))
+            loc = torch.from_numpy(loc).float()
+            locations.append(loc)
+        return locations
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        # load gt image
+        gt_path = self.paths[index]
+        img_bytes = self.file_client.get(gt_path)
+        img_gt = imfrombytes(img_bytes, float32=True)
+
+        # random horizontal flip
+        img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True)
+        h, w, _ = img_gt.shape
+
+        if self.crop_components:
+            locations = self.get_component_coordinates(index, status)
+            loc_left_eye, loc_right_eye, loc_mouth = locations
+
+        # ------------------------ generate lq image ------------------------ #
+        # blur
+        kernel = degradations.random_mixed_kernels(
+            self.kernel_list,
+            self.kernel_prob,
+            self.blur_kernel_size,
+            self.blur_sigma,
+            self.blur_sigma, [-math.pi, math.pi],
+            noise_range=None)
+        img_lq = cv2.filter2D(img_gt, -1, kernel)
+        # downsample
+        scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1])
+        img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR)
+        # noise
+        if self.noise_range is not None:
+            img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range)
+        # jpeg compression
+        if self.jpeg_range is not None:
+            img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range)
+
+        # resize to original size
+        img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR)
+
+        # random color jitter (only for lq)
+        if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob):
+            img_lq = self.color_jitter(img_lq, self.color_jitter_shift)
+        # random to gray (only for lq)
+        if self.gray_prob and np.random.uniform() < self.gray_prob:
+            img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY)
+            img_lq = np.tile(img_lq[:, :, None], [1, 1, 3])
+            if self.opt.get('gt_gray'):
+                img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY)
+                img_gt = np.tile(img_gt[:, :, None], [1, 1, 3])
+
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
+
+        # random color jitter (pytorch version) (only for lq)
+        if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob):
+            brightness = self.opt.get('brightness', (0.5, 1.5))
+            contrast = self.opt.get('contrast', (0.5, 1.5))
+            saturation = self.opt.get('saturation', (0, 1.5))
+            hue = self.opt.get('hue', (-0.1, 0.1))
+            img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue)
+
+        # round and clip
+        img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255.
+
+        # normalize
+        normalize(img_gt, self.mean, self.std, inplace=True)
+        normalize(img_lq, self.mean, self.std, inplace=True)
+
+        if self.crop_components:
+            return_dict = {
+                'lq': img_lq,
+                'gt': img_gt,
+                'gt_path': gt_path,
+                'loc_left_eye': loc_left_eye,
+                'loc_right_eye': loc_right_eye,
+                'loc_mouth': loc_mouth
+            }
+            return return_dict
+        else:
+            return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path}
+
+    def __len__(self):
+        return len(self.paths)

gfpgan/models/__init__.py

@@ -0,0 +1,10 @@
+import importlib
+from basicsr.utils import scandir
+from os import path as osp
+
+# automatically scan and import model modules for registry
+# scan all the files that end with '_model.py' under the model folder
+model_folder = osp.dirname(osp.abspath(__file__))
+model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
+# import all the model modules
+_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]

gfpgan/models/gfpgan_model.py

@@ -0,0 +1,561 @@
+import math
+import os.path as osp
+import torch
+from basicsr.archs import build_network
+from basicsr.losses import build_loss
+from basicsr.losses.losses import r1_penalty
+from basicsr.metrics import calculate_metric
+from basicsr.models.base_model import BaseModel
+from basicsr.utils import get_root_logger, imwrite, tensor2img
+from basicsr.utils.registry import MODEL_REGISTRY
+from collections import OrderedDict
+from torch.nn import functional as F
+from torchvision.ops import roi_align
+from tqdm import tqdm
+
+
+@MODEL_REGISTRY.register()
+class GFPGANModel(BaseModel):
+    """GFPGAN model for <Towards real-world blind face restoratin with generative facial prior>"""
+
+    def __init__(self, opt):
+        super(GFPGANModel, self).__init__(opt)
+        self.idx = 0
+
+        # define network
+        self.net_g = build_network(opt['network_g'])
+        self.net_g = self.model_to_device(self.net_g)
+        self.print_network(self.net_g)
+
+        # load pretrained model
+        load_path = self.opt['path'].get('pretrain_network_g', None)
+        if load_path is not None:
+            param_key = self.opt['path'].get('param_key_g', 'params')
+            self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
+
+        self.log_size = int(math.log(self.opt['network_g']['out_size'], 2))
+
+        if self.is_train:
+            self.init_training_settings()
+
+    def init_training_settings(self):
+        train_opt = self.opt['train']
+
+        # ----------- define net_d ----------- #
+        self.net_d = build_network(self.opt['network_d'])
+        self.net_d = self.model_to_device(self.net_d)
+        self.print_network(self.net_d)
+        # load pretrained model
+        load_path = self.opt['path'].get('pretrain_network_d', None)
+        if load_path is not None:
+            self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
+
+        # ----------- define net_g with Exponential Moving Average (EMA) ----------- #
+        # net_g_ema only used for testing on one GPU and saving
+        # There is no need to wrap with DistributedDataParallel
+        self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
+        # load pretrained model
+        load_path = self.opt['path'].get('pretrain_network_g', None)
+        if load_path is not None:
+            self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
+        else:
+            self.model_ema(0)  # copy net_g weight
+
+        self.net_g.train()
+        self.net_d.train()
+        self.net_g_ema.eval()
+
+        # ----------- facial components networks ----------- #
+        if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt):
+            self.use_facial_disc = True
+        else:
+            self.use_facial_disc = False
+
+        if self.use_facial_disc:
+            # left eye
+            self.net_d_left_eye = build_network(self.opt['network_d_left_eye'])
+            self.net_d_left_eye = self.model_to_device(self.net_d_left_eye)
+            self.print_network(self.net_d_left_eye)
+            load_path = self.opt['path'].get('pretrain_network_d_left_eye')
+            if load_path is not None:
+                self.load_network(self.net_d_left_eye, load_path, True, 'params')
+            # right eye
+            self.net_d_right_eye = build_network(self.opt['network_d_right_eye'])
+            self.net_d_right_eye = self.model_to_device(self.net_d_right_eye)
+            self.print_network(self.net_d_right_eye)
+            load_path = self.opt['path'].get('pretrain_network_d_right_eye')
+            if load_path is not None:
+                self.load_network(self.net_d_right_eye, load_path, True, 'params')
+            # mouth
+            self.net_d_mouth = build_network(self.opt['network_d_mouth'])
+            self.net_d_mouth = self.model_to_device(self.net_d_mouth)
+            self.print_network(self.net_d_mouth)
+            load_path = self.opt['path'].get('pretrain_network_d_mouth')
+            if load_path is not None:
+                self.load_network(self.net_d_mouth, load_path, True, 'params')
+
+            self.net_d_left_eye.train()
+            self.net_d_right_eye.train()
+            self.net_d_mouth.train()
+
+            # ----------- define facial component gan loss ----------- #
+            self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device)
+
+        # ----------- define losses ----------- #
+        if train_opt.get('pixel_opt'):
+            self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
+        else:
+            self.cri_pix = None
+
+        if train_opt.get('perceptual_opt'):
+            self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
+        else:
+            self.cri_perceptual = None
+
+        # L1 loss used in pyramid loss, component style loss and identity loss
+        self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device)
+
+        # gan loss (wgan)
+        self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
+
+        # ----------- define identity loss ----------- #
+        if 'network_identity' in self.opt:
+            self.use_identity = True
+        else:
+            self.use_identity = False
+
+        if self.use_identity:
+            # define identity network
+            self.network_identity = build_network(self.opt['network_identity'])
+            self.network_identity = self.model_to_device(self.network_identity)
+            self.print_network(self.network_identity)
+            load_path = self.opt['path'].get('pretrain_network_identity')
+            if load_path is not None:
+                self.load_network(self.network_identity, load_path, True, None)
+            self.network_identity.eval()
+            for param in self.network_identity.parameters():
+                param.requires_grad = False
+
+        # regularization weights
+        self.r1_reg_weight = train_opt['r1_reg_weight']  # for discriminator
+        self.net_d_iters = train_opt.get('net_d_iters', 1)
+        self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
+        self.net_d_reg_every = train_opt['net_d_reg_every']
+
+        # set up optimizers and schedulers
+        self.setup_optimizers()
+        self.setup_schedulers()
+
+    def setup_optimizers(self):
+        train_opt = self.opt['train']
+
+        # ----------- optimizer g ----------- #
+        net_g_reg_ratio = 1
+        normal_params = []
+        for _, param in self.net_g.named_parameters():
+            normal_params.append(param)
+        optim_params_g = [{  # add normal params first
+            'params': normal_params,
+            'lr': train_opt['optim_g']['lr']
+        }]
+        optim_type = train_opt['optim_g'].pop('type')
+        lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
+        betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)
+        self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
+        self.optimizers.append(self.optimizer_g)
+
+        # ----------- optimizer d ----------- #
+        net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
+        normal_params = []
+        for _, param in self.net_d.named_parameters():
+            normal_params.append(param)
+        optim_params_d = [{  # add normal params first
+            'params': normal_params,
+            'lr': train_opt['optim_d']['lr']
+        }]
+        optim_type = train_opt['optim_d'].pop('type')
+        lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
+        betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio)
+        self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
+        self.optimizers.append(self.optimizer_d)
+
+        if self.use_facial_disc:
+            # setup optimizers for facial component discriminators
+            optim_type = train_opt['optim_component'].pop('type')
+            lr = train_opt['optim_component']['lr']
+            # left eye
+            self.optimizer_d_left_eye = self.get_optimizer(
+                optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99))
+            self.optimizers.append(self.optimizer_d_left_eye)
+            # right eye
+            self.optimizer_d_right_eye = self.get_optimizer(
+                optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99))
+            self.optimizers.append(self.optimizer_d_right_eye)
+            # mouth
+            self.optimizer_d_mouth = self.get_optimizer(
+                optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99))
+            self.optimizers.append(self.optimizer_d_mouth)
+
+    def feed_data(self, data):
+        self.lq = data['lq'].to(self.device)
+        if 'gt' in data:
+            self.gt = data['gt'].to(self.device)
+
+        if 'loc_left_eye' in data:
+            # get facial component locations, shape (batch, 4)
+            self.loc_left_eyes = data['loc_left_eye']
+            self.loc_right_eyes = data['loc_right_eye']
+            self.loc_mouths = data['loc_mouth']
+
+            # uncomment to check data
+            # import torchvision
+            # if self.opt['rank'] == 0:
+            #     import os
+            #     os.makedirs('tmp/gt', exist_ok=True)
+            #     os.makedirs('tmp/lq', exist_ok=True)
+            #     print(self.idx)
+            #     torchvision.utils.save_image(
+            #         self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
+            #     torchvision.utils.save_image(
+            #         self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
+            #     self.idx = self.idx + 1
+
+    def construct_img_pyramid(self):
+        pyramid_gt = [self.gt]
+        down_img = self.gt
+        for _ in range(0, self.log_size - 3):
+            down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False)
+            pyramid_gt.insert(0, down_img)
+        return pyramid_gt
+
+    def get_roi_regions(self, eye_out_size=80, mouth_out_size=120):
+        # hard code
+        face_ratio = int(self.opt['network_g']['out_size'] / 512)
+        eye_out_size *= face_ratio
+        mouth_out_size *= face_ratio
+
+        rois_eyes = []
+        rois_mouths = []
+        for b in range(self.loc_left_eyes.size(0)):  # loop for batch size
+            # left eye and right eye
+            img_inds = self.loc_left_eyes.new_full((2, 1), b)
+            bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0)  # shape: (2, 4)
+            rois = torch.cat([img_inds, bbox], dim=-1)  # shape: (2, 5)
+            rois_eyes.append(rois)
+            # mouse
+            img_inds = self.loc_left_eyes.new_full((1, 1), b)
+            rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1)  # shape: (1, 5)
+            rois_mouths.append(rois)
+
+        rois_eyes = torch.cat(rois_eyes, 0).to(self.device)
+        rois_mouths = torch.cat(rois_mouths, 0).to(self.device)
+
+        # real images
+        all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
+        self.left_eyes_gt = all_eyes[0::2, :, :, :]
+        self.right_eyes_gt = all_eyes[1::2, :, :, :]
+        self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
+        # output
+        all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
+        self.left_eyes = all_eyes[0::2, :, :, :]
+        self.right_eyes = all_eyes[1::2, :, :, :]
+        self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
+
+    def _gram_mat(self, x):
+        """Calculate Gram matrix.
+
+        Args:
+            x (torch.Tensor): Tensor with shape of (n, c, h, w).
+
+        Returns:
+            torch.Tensor: Gram matrix.
+        """
+        n, c, h, w = x.size()
+        features = x.view(n, c, w * h)
+        features_t = features.transpose(1, 2)
+        gram = features.bmm(features_t) / (c * h * w)
+        return gram
+
+    def gray_resize_for_identity(self, out, size=128):
+        out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :])
+        out_gray = out_gray.unsqueeze(1)
+        out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False)
+        return out_gray
+
+    def optimize_parameters(self, current_iter):
+        # optimize net_g
+        for p in self.net_d.parameters():
+            p.requires_grad = False
+        self.optimizer_g.zero_grad()
+
+        if self.use_facial_disc:
+            for p in self.net_d_left_eye.parameters():
+                p.requires_grad = False
+            for p in self.net_d_right_eye.parameters():
+                p.requires_grad = False
+            for p in self.net_d_mouth.parameters():
+                p.requires_grad = False
+
+        # image pyramid loss weight
+        if current_iter < self.opt['train'].get('remove_pyramid_loss', float('inf')):
+            pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 1)
+        else:
+            pyramid_loss_weight = 1e-12  # very small loss
+        if pyramid_loss_weight > 0:
+            self.output, out_rgbs = self.net_g(self.lq, return_rgb=True)
+            pyramid_gt = self.construct_img_pyramid()
+        else:
+            self.output, out_rgbs = self.net_g(self.lq, return_rgb=False)
+
+        # get roi-align regions
+        if self.use_facial_disc:
+            self.get_roi_regions(eye_out_size=80, mouth_out_size=120)
+
+        l_g_total = 0
+        loss_dict = OrderedDict()
+        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
+            # pixel loss
+            if self.cri_pix:
+                l_g_pix = self.cri_pix(self.output, self.gt)
+                l_g_total += l_g_pix
+                loss_dict['l_g_pix'] = l_g_pix
+
+            # image pyramid loss
+            if pyramid_loss_weight > 0:
+                for i in range(0, self.log_size - 2):
+                    l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight
+                    l_g_total += l_pyramid
+                    loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid
+
+            # perceptual loss
+            if self.cri_perceptual:
+                l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
+                if l_g_percep is not None:
+                    l_g_total += l_g_percep
+                    loss_dict['l_g_percep'] = l_g_percep
+                if l_g_style is not None:
+                    l_g_total += l_g_style
+                    loss_dict['l_g_style'] = l_g_style
+
+            # gan loss
+            fake_g_pred = self.net_d(self.output)
+            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
+            l_g_total += l_g_gan
+            loss_dict['l_g_gan'] = l_g_gan
+
+            # facial component loss
+            if self.use_facial_disc:
+                # left eye
+                fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True)
+                l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False)
+                l_g_total += l_g_gan
+                loss_dict['l_g_gan_left_eye'] = l_g_gan
+                # right eye
+                fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True)
+                l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False)
+                l_g_total += l_g_gan
+                loss_dict['l_g_gan_right_eye'] = l_g_gan
+                # mouth
+                fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True)
+                l_g_gan = self.cri_component(fake_mouth, True, is_disc=False)
+                l_g_total += l_g_gan
+                loss_dict['l_g_gan_mouth'] = l_g_gan
+
+                if self.opt['train'].get('comp_style_weight', 0) > 0:
+                    # get gt feat
+                    _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True)
+                    _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True)
+                    _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True)
+
+                    def _comp_style(feat, feat_gt, criterion):
+                        return criterion(self._gram_mat(feat[0]), self._gram_mat(
+                            feat_gt[0].detach())) * 0.5 + criterion(
+                                self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach()))
+
+                    # facial component style loss
+                    comp_style_loss = 0
+                    comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1)
+                    comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1)
+                    comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1)
+                    comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight']
+                    l_g_total += comp_style_loss
+                    loss_dict['l_g_comp_style_loss'] = comp_style_loss
+
+            # identity loss
+            if self.use_identity:
+                identity_weight = self.opt['train']['identity_weight']
+                # get gray images and resize
+                out_gray = self.gray_resize_for_identity(self.output)
+                gt_gray = self.gray_resize_for_identity(self.gt)
+
+                identity_gt = self.network_identity(gt_gray).detach()
+                identity_out = self.network_identity(out_gray)
+                l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight
+                l_g_total += l_identity
+                loss_dict['l_identity'] = l_identity
+
+            l_g_total.backward()
+            self.optimizer_g.step()
+
+        # EMA
+        self.model_ema(decay=0.5**(32 / (10 * 1000)))
+
+        # ----------- optimize net_d ----------- #
+        for p in self.net_d.parameters():
+            p.requires_grad = True
+        self.optimizer_d.zero_grad()
+        if self.use_facial_disc:
+            for p in self.net_d_left_eye.parameters():
+                p.requires_grad = True
+            for p in self.net_d_right_eye.parameters():
+                p.requires_grad = True
+            for p in self.net_d_mouth.parameters():
+                p.requires_grad = True
+            self.optimizer_d_left_eye.zero_grad()
+            self.optimizer_d_right_eye.zero_grad()
+            self.optimizer_d_mouth.zero_grad()
+
+        fake_d_pred = self.net_d(self.output.detach())
+        real_d_pred = self.net_d(self.gt)
+        l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True)
+        loss_dict['l_d'] = l_d
+        # In wgan, real_score should be positive and fake_score should benegative
+        loss_dict['real_score'] = real_d_pred.detach().mean()
+        loss_dict['fake_score'] = fake_d_pred.detach().mean()
+        l_d.backward()
+
+        if current_iter % self.net_d_reg_every == 0:
+            self.gt.requires_grad = True
+            real_pred = self.net_d(self.gt)
+            l_d_r1 = r1_penalty(real_pred, self.gt)
+            l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
+            loss_dict['l_d_r1'] = l_d_r1.detach().mean()
+            l_d_r1.backward()
+
+        self.optimizer_d.step()
+
+        if self.use_facial_disc:
+            # lefe eye
+            fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach())
+            real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt)
+            l_d_left_eye = self.cri_component(
+                real_d_pred, True, is_disc=True) + self.cri_gan(
+                    fake_d_pred, False, is_disc=True)
+            loss_dict['l_d_left_eye'] = l_d_left_eye
+            l_d_left_eye.backward()
+            # right eye
+            fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach())
+            real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt)
+            l_d_right_eye = self.cri_component(
+                real_d_pred, True, is_disc=True) + self.cri_gan(
+                    fake_d_pred, False, is_disc=True)
+            loss_dict['l_d_right_eye'] = l_d_right_eye
+            l_d_right_eye.backward()
+            # mouth
+            fake_d_pred, _ = self.net_d_mouth(self.mouths.detach())
+            real_d_pred, _ = self.net_d_mouth(self.mouths_gt)
+            l_d_mouth = self.cri_component(
+                real_d_pred, True, is_disc=True) + self.cri_gan(
+                    fake_d_pred, False, is_disc=True)
+            loss_dict['l_d_mouth'] = l_d_mouth
+            l_d_mouth.backward()
+
+            self.optimizer_d_left_eye.step()
+            self.optimizer_d_right_eye.step()
+            self.optimizer_d_mouth.step()
+
+        self.log_dict = self.reduce_loss_dict(loss_dict)
+
+    def test(self):
+        with torch.no_grad():
+            if hasattr(self, 'net_g_ema'):
+                self.net_g_ema.eval()
+                self.output, _ = self.net_g_ema(self.lq)
+            else:
+                logger = get_root_logger()
+                logger.warning('Do not have self.net_g_ema, use self.net_g.')
+                self.net_g.eval()
+                self.output, _ = self.net_g(self.lq)
+                self.net_g.train()
+
+    def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        if self.opt['rank'] == 0:
+            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
+
+    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        dataset_name = dataloader.dataset.opt['name']
+        with_metrics = self.opt['val'].get('metrics') is not None
+        if with_metrics:
+            self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
+        pbar = tqdm(total=len(dataloader), unit='image')
+
+        for idx, val_data in enumerate(dataloader):
+            img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
+            self.feed_data(val_data)
+            self.test()
+
+            visuals = self.get_current_visuals()
+            sr_img = tensor2img([visuals['sr']], min_max=(-1, 1))
+            gt_img = tensor2img([visuals['gt']], min_max=(-1, 1))
+
+            if 'gt' in visuals:
+                gt_img = tensor2img([visuals['gt']], min_max=(-1, 1))
+                del self.gt
+            # tentative for out of GPU memory
+            del self.lq
+            del self.output
+            torch.cuda.empty_cache()
+
+            if save_img:
+                if self.opt['is_train']:
+                    save_img_path = osp.join(self.opt['path']['visualization'], img_name,
+                                             f'{img_name}_{current_iter}.png')
+                else:
+                    if self.opt['val']['suffix']:
+                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
+                                                 f'{img_name}_{self.opt["val"]["suffix"]}.png')
+                    else:
+                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
+                                                 f'{img_name}_{self.opt["name"]}.png')
+                imwrite(sr_img, save_img_path)
+
+            if with_metrics:
+                # calculate metrics
+                for name, opt_ in self.opt['val']['metrics'].items():
+                    metric_data = dict(img1=sr_img, img2=gt_img)
+                    self.metric_results[name] += calculate_metric(metric_data, opt_)
+            pbar.update(1)
+            pbar.set_description(f'Test {img_name}')
+        pbar.close()
+
+        if with_metrics:
+            for metric in self.metric_results.keys():
+                self.metric_results[metric] /= (idx + 1)
+
+            self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
+
+    def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
+        log_str = f'Validation {dataset_name}\n'
+        for metric, value in self.metric_results.items():
+            log_str += f'\t # {metric}: {value:.4f}\n'
+        logger = get_root_logger()
+        logger.info(log_str)
+        if tb_logger:
+            for metric, value in self.metric_results.items():
+                tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
+
+    def get_current_visuals(self):
+        out_dict = OrderedDict()
+        out_dict['gt'] = self.gt.detach().cpu()
+        out_dict['sr'] = self.output.detach().cpu()
+        return out_dict
+
+    def save(self, epoch, current_iter):
+        self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
+        self.save_network(self.net_d, 'net_d', current_iter)
+        # save component discriminators
+        if self.use_facial_disc:
+            self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter)
+            self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter)
+            self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter)
+        self.save_training_state(epoch, current_iter)

gfpgan/train.py

@@ -0,0 +1,11 @@
+# flake8: noqa
+import os.path as osp
+from basicsr.train import train_pipeline
+
+import gfpgan.archs
+import gfpgan.data
+import gfpgan.models
+
+if __name__ == '__main__':
+    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
+    train_pipeline(root_path)

gfpgan/utils.py

@@ -0,0 +1,134 @@
+import cv2
+import os
+import torch
+from basicsr.utils import img2tensor, tensor2img
+from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+from torch.hub import download_url_to_file, get_dir
+from torchvision.transforms.functional import normalize
+from urllib.parse import urlparse
+
+from gfpgan.archs.gfpganv1_arch import GFPGANv1
+from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class GFPGANer():
+
+    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None):
+        self.upscale = upscale
+        self.bg_upsampler = bg_upsampler
+
+        # initialize model
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        # initialize the GFP-GAN
+        if arch == 'clean':
+            self.gfpgan = GFPGANv1Clean(
+                out_size=512,
+                num_style_feat=512,
+                channel_multiplier=channel_multiplier,
+                decoder_load_path=None,
+                fix_decoder=False,
+                num_mlp=8,
+                input_is_latent=True,
+                different_w=True,
+                narrow=1,
+                sft_half=True)
+        else:
+            self.gfpgan = GFPGANv1(
+                out_size=512,
+                num_style_feat=512,
+                channel_multiplier=channel_multiplier,
+                decoder_load_path=None,
+                fix_decoder=True,
+                num_mlp=8,
+                input_is_latent=True,
+                different_w=True,
+                narrow=1,
+                sft_half=True)
+        # initialize face helper
+        self.face_helper = FaceRestoreHelper(
+            upscale,
+            face_size=512,
+            crop_ratio=(1, 1),
+            det_model='retinaface_resnet50',
+            save_ext='png',
+            device=self.device)
+
+        if model_path.startswith('https://'):
+            model_path = load_file_from_url(url=model_path, model_dir='gfpgan/weights', progress=True, file_name=None)
+        loadnet = torch.load(model_path)
+        if 'params_ema' in loadnet:
+            keyname = 'params_ema'
+        else:
+            keyname = 'params'
+        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
+        self.gfpgan.eval()
+        self.gfpgan = self.gfpgan.to(self.device)
+
+    @torch.no_grad()
+    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
+        self.face_helper.clean_all()
+
+        if has_aligned:
+            img = cv2.resize(img, (512, 512))
+            self.face_helper.cropped_faces = [img]
+        else:
+            self.face_helper.read_image(img)
+            # get face landmarks for each face
+            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face)
+            # align and warp each face
+            self.face_helper.align_warp_face()
+
+        # face restoration
+        for cropped_face in self.face_helper.cropped_faces:
+            # prepare data
+            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
+
+            try:
+                output = self.gfpgan(cropped_face_t, return_rgb=False)[0]
+                # convert to image
+                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
+            except RuntimeError as error:
+                print(f'\tFailed inference for GFPGAN: {error}.')
+                restored_face = cropped_face
+
+            restored_face = restored_face.astype('uint8')
+            self.face_helper.add_restored_face(restored_face)
+
+        if not has_aligned and paste_back:
+
+            if self.bg_upsampler is not None:
+                # Now only support RealESRGAN
+                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
+            else:
+                bg_img = None
+
+            self.face_helper.get_inverse_affine(None)
+            # paste each restored face to the input image
+            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
+            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
+        else:
+            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
+    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    """
+    if model_dir is None:
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file

gfpgan/weights/README.md

@@ -0,0 +1,3 @@
+# Weights
+
+Put the downloaded weights to this folder.

inference_gfpgan.py

@@ -0,0 +1,98 @@
+import argparse
+import cv2
+import glob
+import numpy as np
+import os
+import torch
+from basicsr.utils import imwrite
+
+from gfpgan import GFPGANer
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--upscale', type=int, default=2)
+    parser.add_argument('--arch', type=str, default='clean')
+    parser.add_argument('--channel', type=int, default=2)
+    parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth')
+    parser.add_argument('--bg_upsampler', type=str, default='realesrgan')
+    parser.add_argument('--bg_tile', type=int, default=400)
+    parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
+    parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
+    parser.add_argument('--only_center_face', action='store_true')
+    parser.add_argument('--aligned', action='store_true')
+    parser.add_argument('--paste_back', action='store_false')
+    parser.add_argument('--save_root', type=str, default='results')
+
+    args = parser.parse_args()
+    if args.test_path.endswith('/'):
+        args.test_path = args.test_path[:-1]
+    os.makedirs(args.save_root, exist_ok=True)
+
+    # background upsampler
+    if args.bg_upsampler == 'realesrgan':
+        if not torch.cuda.is_available():  # CPU
+            import warnings
+            warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
+                          'If you really want to use it, please modify the corresponding codes.')
+            bg_upsampler = None
+        else:
+            from realesrgan import RealESRGANer
+            bg_upsampler = RealESRGANer(
+                scale=2,
+                model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+                tile=args.bg_tile,
+                tile_pad=10,
+                pre_pad=0,
+                half=True)  # need to set False in CPU mode
+    else:
+        bg_upsampler = None
+    # set up GFPGAN restorer
+    restorer = GFPGANer(
+        model_path=args.model_path,
+        upscale=args.upscale,
+        arch=args.arch,
+        channel_multiplier=args.channel,
+        bg_upsampler=bg_upsampler)
+
+    img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
+    for img_path in img_list:
+        # read image
+        img_name = os.path.basename(img_path)
+        print(f'Processing {img_name} ...')
+        basename, ext = os.path.splitext(img_name)
+        input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+
+        cropped_faces, restored_faces, restored_img = restorer.enhance(
+            input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
+
+        # save faces
+        for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
+            # save cropped face
+            save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
+            imwrite(cropped_face, save_crop_path)
+            # save restored face
+            if args.suffix is not None:
+                save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
+            else:
+                save_face_name = f'{basename}_{idx:02d}.png'
+            save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name)
+            imwrite(restored_face, save_restore_path)
+            # save cmp image
+            cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
+            imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png'))
+
+        # save restored img
+        if restored_img is not None:
+            if args.suffix is not None:
+                save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}_{args.suffix}{ext}')
+            else:
+                save_restore_path = os.path.join(args.save_root, 'restored_imgs', img_name)
+            imwrite(restored_img, save_restore_path)
+
+    print(f'Results are in the [{args.save_root}] folder.')
+
+
+if __name__ == '__main__':
+    main()

options/train_gfpgan_v1.yml

@@ -0,0 +1,216 @@
+# general settings
+name: train_GFPGANv1_512
+model_type: GFPGANModel
+num_gpu: 4
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: FFHQ
+    type: FFHQDegradationDataset
+    # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
+    dataroot_gt: datasets/ffhq/ffhq_512
+    io_backend:
+      # type: lmdb
+      type: disk
+
+    use_hflip: true
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+    out_size: 512
+
+    blur_kernel_size: 41
+    kernel_list: ['iso', 'aniso']
+    kernel_prob: [0.5, 0.5]
+    blur_sigma: [0.1, 10]
+    downsample_range: [0.8, 8]
+    noise_range: [0, 20]
+    jpeg_range: [60, 100]
+
+    # color jitter and gray
+    color_jitter_prob: 0.3
+    color_jitter_shift: 20
+    color_jitter_pt_prob: 0.3
+    gray_prob: 0.01
+
+    # If you do not want colorization, please set
+    # color_jitter_prob: ~
+    # color_jitter_pt_prob: ~
+    # gray_prob: 0.01
+    # gt_gray: True
+
+    crop_components: true
+    component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
+    eye_enlarge_ratio: 1.4
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 3
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    # Please modify accordingly to use your own validation
+    # Or comment the val block if do not need validation during training
+    name: validation
+    type: PairedImageDataset
+    dataroot_lq: datasets/faces/validation/input
+    dataroot_gt: datasets/faces/validation/reference
+    io_backend:
+      type: disk
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+    scale: 1
+
+# network structures
+network_g:
+  type: GFPGANv1
+  out_size: 512
+  num_style_feat: 512
+  channel_multiplier: 1
+  resample_kernel: [1, 3, 3, 1]
+  decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
+  fix_decoder: true
+  num_mlp: 8
+  lr_mlp: 0.01
+  input_is_latent: true
+  different_w: true
+  narrow: 1
+  sft_half: true
+
+network_d:
+  type: StyleGAN2Discriminator
+  out_size: 512
+  channel_multiplier: 1
+  resample_kernel: [1, 3, 3, 1]
+
+network_d_left_eye:
+  type: FacialComponentDiscriminator
+
+network_d_right_eye:
+  type: FacialComponentDiscriminator
+
+network_d_mouth:
+  type: FacialComponentDiscriminator
+
+network_identity:
+  type: ResNetArcFace
+  block: IRBlock
+  layers: [2, 2, 2, 2]
+  use_se: False
+
+# path
+path:
+  pretrain_network_g: ~
+  param_key_g: params_ema
+  strict_load_g: ~
+  pretrain_network_d: ~
+  pretrain_network_d_left_eye: ~
+  pretrain_network_d_right_eye: ~
+  pretrain_network_d_mouth: ~
+  pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth
+  # resume
+  resume_state: ~
+  ignore_resume_networks: ['network_identity']
+
+# training settings
+train:
+  optim_g:
+    type: Adam
+    lr: !!float 2e-3
+  optim_d:
+    type: Adam
+    lr: !!float 2e-3
+  optim_component:
+    type: Adam
+    lr: !!float 2e-3
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [600000, 700000]
+    gamma: 0.5
+
+  total_iter: 800000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  # pixel loss
+  pixel_opt:
+    type: L1Loss
+    loss_weight: !!float 1e-1
+    reduction: mean
+  # L1 loss used in pyramid loss, component style loss and identity loss
+  L1_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+
+  # image pyramid loss
+  pyramid_loss_weight: 1
+  remove_pyramid_loss: 50000
+  # perceptual loss (content and style losses)
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      # before relu
+      'conv1_2': 0.1
+      'conv2_2': 0.1
+      'conv3_4': 1
+      'conv4_4': 1
+      'conv5_4': 1
+    vgg_type: vgg19
+    use_input_norm: true
+    perceptual_weight: !!float 1
+    style_weight: 50
+    range_norm: true
+    criterion: l1
+  # gan loss
+  gan_opt:
+    type: GANLoss
+    gan_type: wgan_softplus
+    loss_weight: !!float 1e-1
+  # r1 regularization for discriminator
+  r1_reg_weight: 10
+  # facial component loss
+  gan_component_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 1
+  comp_style_weight: 200
+  # identity loss
+  identity_weight: 10
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+  net_d_reg_every: 16
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: true
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500
+
+find_unused_parameters: true

options/train_gfpgan_v1_simple.yml

@@ -0,0 +1,216 @@
+# general settings
+name: train_GFPGANv1_512_simple
+model_type: GFPGANModel
+num_gpu: 4
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: FFHQ
+    type: FFHQDegradationDataset
+    # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
+    dataroot_gt: datasets/ffhq/ffhq_512
+    io_backend:
+      # type: lmdb
+      type: disk
+
+    use_hflip: true
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+    out_size: 512
+
+    blur_kernel_size: 41
+    kernel_list: ['iso', 'aniso']
+    kernel_prob: [0.5, 0.5]
+    blur_sigma: [0.1, 10]
+    downsample_range: [0.8, 8]
+    noise_range: [0, 20]
+    jpeg_range: [60, 100]
+
+    # color jitter and gray
+    color_jitter_prob: 0.3
+    color_jitter_shift: 20
+    color_jitter_pt_prob: 0.3
+    gray_prob: 0.01
+
+    # If you do not want colorization, please set
+    # color_jitter_prob: ~
+    # color_jitter_pt_prob: ~
+    # gray_prob: 0.01
+    # gt_gray: True
+
+    # crop_components: false
+    # component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
+    # eye_enlarge_ratio: 1.4
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 3
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    # Please modify accordingly to use your own validation
+    # Or comment the val block if do not need validation during training
+    name: validation
+    type: PairedImageDataset
+    dataroot_lq: datasets/faces/validation/input
+    dataroot_gt: datasets/faces/validation/reference
+    io_backend:
+      type: disk
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+    scale: 1
+
+# network structures
+network_g:
+  type: GFPGANv1
+  out_size: 512
+  num_style_feat: 512
+  channel_multiplier: 1
+  resample_kernel: [1, 3, 3, 1]
+  decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
+  fix_decoder: true
+  num_mlp: 8
+  lr_mlp: 0.01
+  input_is_latent: true
+  different_w: true
+  narrow: 1
+  sft_half: true
+
+network_d:
+  type: StyleGAN2Discriminator
+  out_size: 512
+  channel_multiplier: 1
+  resample_kernel: [1, 3, 3, 1]
+
+# network_d_left_eye:
+#   type: FacialComponentDiscriminator
+
+# network_d_right_eye:
+#   type: FacialComponentDiscriminator
+
+# network_d_mouth:
+#   type: FacialComponentDiscriminator
+
+network_identity:
+  type: ResNetArcFace
+  block: IRBlock
+  layers: [2, 2, 2, 2]
+  use_se: False
+
+# path
+path:
+  pretrain_network_g: ~
+  param_key_g: params_ema
+  strict_load_g: ~
+  pretrain_network_d: ~
+  # pretrain_network_d_left_eye: ~
+  # pretrain_network_d_right_eye: ~
+  # pretrain_network_d_mouth: ~
+  pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth
+  # resume
+  resume_state: ~
+  ignore_resume_networks: ['network_identity']
+
+# training settings
+train:
+  optim_g:
+    type: Adam
+    lr: !!float 2e-3
+  optim_d:
+    type: Adam
+    lr: !!float 2e-3
+  optim_component:
+    type: Adam
+    lr: !!float 2e-3
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [600000, 700000]
+    gamma: 0.5
+
+  total_iter: 800000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  # pixel loss
+  pixel_opt:
+    type: L1Loss
+    loss_weight: !!float 1e-1
+    reduction: mean
+  # L1 loss used in pyramid loss, component style loss and identity loss
+  L1_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+
+  # image pyramid loss
+  pyramid_loss_weight: 1
+  remove_pyramid_loss: 50000
+  # perceptual loss (content and style losses)
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      # before relu
+      'conv1_2': 0.1
+      'conv2_2': 0.1
+      'conv3_4': 1
+      'conv4_4': 1
+      'conv5_4': 1
+    vgg_type: vgg19
+    use_input_norm: true
+    perceptual_weight: !!float 1
+    style_weight: 50
+    range_norm: true
+    criterion: l1
+  # gan loss
+  gan_opt:
+    type: GANLoss
+    gan_type: wgan_softplus
+    loss_weight: !!float 1e-1
+  # r1 regularization for discriminator
+  r1_reg_weight: 10
+  # facial component loss
+  # gan_component_opt:
+  #   type: GANLoss
+  #   gan_type: vanilla
+  #   real_label_val: 1.0
+  #   fake_label_val: 0.0
+  #   loss_weight: !!float 1
+  # comp_style_weight: 200
+  # identity loss
+  identity_weight: 10
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+  net_d_reg_every: 16
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: true
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500
+
+find_unused_parameters: true

requirements.txt

@@ -0,0 +1,11 @@
+basicsr>=1.3.3.10
+facexlib>=0.2.0.2
+lmdb
+numpy
+opencv-python
+pyyaml
+tb-nightly
+torch>=1.7
+torchvision
+tqdm
+yapf

scripts/parse_landmark.py

@@ -0,0 +1,77 @@
+import cv2
+import json
+import numpy as np
+import torch
+from basicsr.utils import FileClient, imfrombytes
+from collections import OrderedDict
+
+print('Load JSON metadata...')
+# use the json file in FFHQ dataset
+with open('ffhq-dataset-v2.json', 'rb') as f:
+    json_data = json.load(f, object_pairs_hook=OrderedDict)
+
+print('Open LMDB file...')
+# read ffhq images
+file_client = FileClient('lmdb', db_paths='datasets/ffhq/ffhq_512.lmdb')
+with open('datasets/ffhq/ffhq_512.lmdb/meta_info.txt') as fin:
+    paths = [line.split('.')[0] for line in fin]
+
+save_img = False
+scale = 0.5  # 0.5 for official FFHQ (512x512), 1 for others
+enlarge_ratio = 1.4  # only for eyes
+save_dict = {}
+
+for item_idx, item in enumerate(json_data.values()):
+    print(f'\r{item_idx} / {len(json_data)}, {item["image"]["file_path"]} ', end='', flush=True)
+
+    # parse landmarks
+    lm = np.array(item['image']['face_landmarks'])
+    lm = lm * scale
+
+    item_dict = {}
+    # get image
+    if save_img:
+        img_bytes = file_client.get(paths[item_idx])
+        img = imfrombytes(img_bytes, float32=True)
+
+    map_left_eye = list(range(36, 42))
+    map_right_eye = list(range(42, 48))
+    map_mouth = list(range(48, 68))
+
+    # eye_left
+    mean_left_eye = np.mean(lm[map_left_eye], 0)  # (x, y)
+    half_len_left_eye = np.max((np.max(np.max(lm[map_left_eye], 0) - np.min(lm[map_left_eye], 0)) / 2, 16))
+    item_dict['left_eye'] = [mean_left_eye[0], mean_left_eye[1], half_len_left_eye]
+    # mean_left_eye[0] = 512 - mean_left_eye[0]  # for testing flip
+    half_len_left_eye *= enlarge_ratio
+    loc_left_eye = np.hstack((mean_left_eye - half_len_left_eye + 1, mean_left_eye + half_len_left_eye)).astype(int)
+    if save_img:
+        eye_left_img = img[loc_left_eye[1]:loc_left_eye[3], loc_left_eye[0]:loc_left_eye[2], :]
+        cv2.imwrite(f'tmp/{item_idx:08d}_eye_left.png', eye_left_img * 255)
+
+    # eye_right
+    mean_right_eye = np.mean(lm[map_right_eye], 0)
+    half_len_right_eye = np.max((np.max(np.max(lm[map_right_eye], 0) - np.min(lm[map_right_eye], 0)) / 2, 16))
+    item_dict['right_eye'] = [mean_right_eye[0], mean_right_eye[1], half_len_right_eye]
+    # mean_right_eye[0] = 512 - mean_right_eye[0]  # # for testing flip
+    half_len_right_eye *= enlarge_ratio
+    loc_right_eye = np.hstack(
+        (mean_right_eye - half_len_right_eye + 1, mean_right_eye + half_len_right_eye)).astype(int)
+    if save_img:
+        eye_right_img = img[loc_right_eye[1]:loc_right_eye[3], loc_right_eye[0]:loc_right_eye[2], :]
+        cv2.imwrite(f'tmp/{item_idx:08d}_eye_right.png', eye_right_img * 255)
+
+    # mouth
+    mean_mouth = np.mean(lm[map_mouth], 0)
+    half_len_mouth = np.max((np.max(np.max(lm[map_mouth], 0) - np.min(lm[map_mouth], 0)) / 2, 16))
+    item_dict['mouth'] = [mean_mouth[0], mean_mouth[1], half_len_mouth]
+    # mean_mouth[0] = 512 - mean_mouth[0]  # for testing flip
+    loc_mouth = np.hstack((mean_mouth - half_len_mouth + 1, mean_mouth + half_len_mouth)).astype(int)
+    if save_img:
+        mouth_img = img[loc_mouth[1]:loc_mouth[3], loc_mouth[0]:loc_mouth[2], :]
+        cv2.imwrite(f'tmp/{item_idx:08d}_mouth.png', mouth_img * 255)
+
+    save_dict[f'{item_idx:08d}'] = item_dict
+
+print('Save...')
+torch.save(save_dict, './FFHQ_eye_mouth_landmarks_512.pth')

setup.cfg

@@ -0,0 +1,22 @@
+[flake8]
+ignore =
+    # line break before binary operator (W503)
+    W503,
+    # line break after binary operator (W504)
+    W504,
+max-line-length=120
+
+[yapf]
+based_on_style = pep8
+column_limit = 120
+blank_line_before_nested_class_or_def = true
+split_before_expression_after_opening_paren = true
+
+[isort]
+line_length = 120
+multi_line_output = 0
+known_standard_library = pkg_resources,setuptools
+known_first_party = gfpgan
+known_third_party = basicsr,cv2,facexlib,numpy,torch,torchvision,tqdm
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY

setup.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+
+from setuptools import find_packages, setup
+
+import os
+import subprocess
+import time
+
+version_file = 'gfpgan/version.py'
+
+
+def readme():
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+def get_git_hash():
+
+    def _minimal_ext_cmd(cmd):
+        # construct minimal environment
+        env = {}
+        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
+            v = os.environ.get(k)
+            if v is not None:
+                env[k] = v
+        # LANGUAGE is used on win32
+        env['LANGUAGE'] = 'C'
+        env['LANG'] = 'C'
+        env['LC_ALL'] = 'C'
+        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
+        return out
+
+    try:
+        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
+        sha = out.strip().decode('ascii')
+    except OSError:
+        sha = 'unknown'
+
+    return sha
+
+
+def get_hash():
+    if os.path.exists('.git'):
+        sha = get_git_hash()[:7]
+    elif os.path.exists(version_file):
+        try:
+            from facexlib.version import __version__
+            sha = __version__.split('+')[-1]
+        except ImportError:
+            raise ImportError('Unable to get git version')
+    else:
+        sha = 'unknown'
+
+    return sha
+
+
+def write_version_py():
+    content = """# GENERATED VERSION FILE
+# TIME: {}
+__version__ = '{}'
+__gitsha__ = '{}'
+version_info = ({})
+"""
+    sha = get_hash()
+    with open('VERSION', 'r') as f:
+        SHORT_VERSION = f.read().strip()
+    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
+
+    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
+    with open(version_file, 'w') as f:
+        f.write(version_file_str)
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def get_requirements(filename='requirements.txt'):
+    here = os.path.dirname(os.path.realpath(__file__))
+    with open(os.path.join(here, filename), 'r') as f:
+        requires = [line.replace('\n', '') for line in f.readlines()]
+    return requires
+
+
+if __name__ == '__main__':
+    write_version_py()
+    setup(
+        name='gfpgan',
+        version=get_version(),
+        description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration',
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        author='Xintao Wang',
+        author_email='xintao.wang@outlook.com',
+        keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan',
+        url='https://github.com/TencentARC/GFPGAN',
+        include_package_data=True,
+        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
+        classifiers=[
+            'Development Status :: 4 - Beta',
+            'License :: OSI Approved :: Apache Software License',
+            'Operating System :: OS Independent',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+        ],
+        license='Apache License Version 2.0',
+        setup_requires=['cython', 'numpy'],
+        install_requires=get_requirements(),
+        zip_safe=False)