Merge pull request #33 from TencentARC/readme

Update ReadMe

PaperModel.md

@@ -0,0 +1,74 @@
+# 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
+
+    # 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
+    ```
+
+## :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_full.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_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1 --aligned
+    ```
+
+- Option 2: Have successfully compiled extensions during installation
+
+    ```bash
+    python inference_gfpgan_full.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_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1 --aligned
+    ```

README.md

@@ -5,31 +5,23 @@
 [![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
 [![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
 
-[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
+1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
+1. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
 
-GFPGAN is a blind face restoration algorithm towards real-world face images.
+GFPGAN aims at developing **Practical Algorithm for Real-world Face Restoration**.<br>
+It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration.
 
-<a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
+:triangular_flag_on_post: **Updates**
-[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
+
+- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensionts.
+- :white_check_mark: We provide an updated model without colorizing faces.
 
 ### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
+
 > [[Paper](https://arxiv.org/abs/2101.04061)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [Demo] <br>
 > [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
 > Applied Research Center (ARC), Tencent PCG
 
-#### Abstract
-
-Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
-
-#### BibTeX
-
-    @InProceedings{wang2021gfpgan,
-        author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
-        title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
-        booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-        year = {2021}
-    }
-
 <p align="center">
   <img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
 </p>
@@ -40,25 +32,23 @@ Blind face restoration usually relies on facial priors, such as facial geometry
 
 - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
 - [PyTorch >= 1.7](https://pytorch.org/)
-- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
+- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
-- Linux (We have not tested on Windows)
+- Option: Linux (We have not tested on Windows)
 
 ### Installation
 
+We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. <br>
+If you want want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation.
+
 1. Clone repo
 
     ```bash
-    git clone https://github.com/xinntao/GFPGAN.git
+    git clone https://github.com/TencentARC/GFPGAN.git
     cd GFPGAN
     ```
 
 1. Install dependent packages
 
-    As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load then 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
@@ -68,58 +58,43 @@ Blind face restoration usually relies on facial priors, such as facial geometry
     # We use face detection and face restoration helper in the facexlib package
     pip install facexlib
 
-    pip install -r requirements.txt
-
-    # 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
     ```
 
 ## :zap: Quick Inference
 
-Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
+Download pre-trained models: [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
 
 ```bash
-wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
+wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models
 ```
 
-- Option 1: Load extensions just-in-time(JIT)
+**Inference!**
 
 ```bash
-    BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
+python inference_gfpgan_full.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results
-
-    # for aligned images
-    BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
 ```
 
-- Option 2: Have successfully compiled extensions during installation
+## :european_castle: Model Zoo
 
-    ```bash
+- [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
-    python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
+- [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
-
-    # for aligned images
-    python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
-    ```
 
 ## :computer: Training
 
-We provide complete training codes for GFPGAN. <br>
+We provide the training codes for GFPGAN (used in our paper). <br>
 You could improve it according to your own needs.
 
+**Tips**
+
+1. More high quality faces can improve the restoration quality.
+2. You may need to perform some pre-processing, such as beauty makeup.
+
+
+**Procedures**
+
+(You can try a simple version ( `train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
+
 1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
 
 1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
@@ -133,13 +108,18 @@ You could improve it according to your own needs.
 
 > python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
 
-or load extensions just-in-time(JIT)
-
-> BASICSR_JIT=True python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
-
 ## :scroll: License and Acknowledgement
 
-GFPGAN is realeased under Apache License Version 2.0.
+GFPGAN is released under Apache License Version 2.0.
+
+## BibTeX
+
+    @InProceedings{wang2021gfpgan,
+        author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
+        title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
+        booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+        year = {2021}
+    }
 
 ## :e-mail: Contact
 

README_CN.md

@@ -1,103 +0,0 @@
-# GFPGAN (CVPR 2021)
-
-[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
-
-GFPGAN is a blind face restoration algorithm towards real-world face images.
-
-<a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
-[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
-
-### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
-> [[Paper](https://arxiv.org/abs/2101.04061)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [Demo] <br>
-> [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
-> Applied Research Center (ARC), Tencent PCG
-
-#### Abstract
-
-Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
-
-#### BibTeX
-
-    @InProceedings{wang2021gfpgan,
-        author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
-        title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
-        booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-        year = {2021}
-    }
-
-<p align="center">
-  <img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
-</p>
-
----
-
-## :wrench: Dependencies and Installation
-
-- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
-- [PyTorch >= 1.7](https://pytorch.org/)
-- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
-
-### Installation
-
-1. Clone repo
-
-    ```bash
-    git clone https://github.com/xinntao/GFPGAN.git
-    cd GFPGAN
-    ```
-
-1. Install dependent packages
-
-    ```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
-    BASICSR_EXT=True 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
-    ```
-
-## :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
-```
-
-```bash
-python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
-
-# for aligned images
-python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
-```
-
-## :computer: Training
-
-We provide complete training codes for GFPGAN. <br>
-You could improve it according to your own needs.
-
-1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
-
-1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` 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)
-
-1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
-
-1. Training
-
-> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
-
-## :scroll: License and Acknowledgement
-
-GFPGAN is realeased under Apache License Version 2.0.
-
-## :e-mail: Contact
-
-If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.

train_gfpgan_v1.yml

@@ -34,6 +34,12 @@ datasets:
     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
@@ -42,7 +48,7 @@ datasets:
     use_shuffle: true
     num_worker_per_gpu: 6
     batch_size_per_gpu: 3
-    dataset_enlarge_ratio: 100
+    dataset_enlarge_ratio: 1
     prefetch_mode: ~
 
   val:

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