diff --git a/PaperModel.md b/PaperModel.md
new file mode 100644
index 00000000..00f500fe
--- /dev/null
+++ b/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.
+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
+ ```
diff --git a/README.md b/README.md
index c7263bfe..8dede821 100644
--- a/README.md
+++ b/README.md
@@ -5,31 +5,23 @@
[](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
[](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 
+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**.
+It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration.
-
-[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
+:triangular_flag_on_post: **Updates**
+
+- :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)] [[Project Page](https://xinntao.github.io/projects/gfpgan)] [Demo]
> [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)
> 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}
- }
-
@@ -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)
-- Linux (We have not tested on Windows)
+- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
+- Option: Linux (We have not tested on Windows)
### Installation
+We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions.
+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
@@ -69,56 +59,41 @@ Blind face restoration usually relies on facial priors, such as facial geometry
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
+## :zap: Quick Inference
- pip install -r requirements.txt
- ```
+Download pre-trained models: [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
-## :zap: Quick Inference
+```bash
+wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models
+```
-Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
+**Inference!**
```bash
-wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
+python inference_gfpgan_full.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results
```
-- Option 1: Load extensions just-in-time(JIT)
+## :european_castle: Model Zoo
- ```bash
- BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
+- [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
+- [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
- # for aligned images
- BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
- ```
+## :computer: Training
-- Option 2: Have successfully compiled extensions during installation
+We provide the training codes for GFPGAN (used in our paper).
+You could improve it according to your own needs.
- ```bash
- python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
+**Tips**
- # for aligned images
- python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
- ```
+1. More high quality faces can improve the restoration quality.
+2. You may need to perform some pre-processing, such as beauty makeup.
-## :computer: Training
-We provide complete training codes for GFPGAN.
-You could improve it according to your own needs.
+**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)
@@ -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)
+## :scroll: License and Acknowledgement
-> BASICSR_JIT=True python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
+GFPGAN is released under Apache License Version 2.0.
-## :scroll: License and Acknowledgement
+## BibTeX
-GFPGAN is realeased under Apache License Version 2.0.
+ @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
diff --git a/README_CN.md b/README_CN.md
deleted file mode 100644
index 462fed3f..00000000
--- a/README_CN.md
+++ /dev/null
@@ -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.
-
-
-[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)] [[Project Page](https://xinntao.github.io/projects/gfpgan)] [Demo]
-> [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)
-> 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}
- }
-
-
-
-
-
----
-
-## :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.
-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`.
diff --git a/train_gfpgan_v1.yml b/train_gfpgan_v1.yml
index 4fe6e5c1..0997d7cd 100644
--- a/train_gfpgan_v1.yml
+++ b/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:
diff --git a/train_gfpgan_v1_simple.yml b/train_gfpgan_v1_simple.yml
new file mode 100644
index 00000000..8bf4f089
--- /dev/null
+++ b/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