Note that this version is not compatible with previous versions. If you want to use previous ones, please refer to the old_version branch.

English | 简体中文   GitHub | Gitee码云

Google Colab: GitHub Link | Google Drive Link
Ⓜ️ Model Zoo ⏬ Google Drive: Pretrained Models | Reproduced Experiments ⏬ 百度网盘: 预训练模型 | 复现实验
📁 Datasets ⏬ Google Drive ⏬ 百度网盘 (提取码:basr)
📈 Training curves in wandb
💻 Commands for training and testing
⚡ HOWTOs

BasicSR (Basic Super Restoration) is an open source image and video restoration toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, etc.
_{(ESRGAN, EDVR, DNI, SFTGAN) (HandyView, HandyFigure, HandyCrawler, HandyWriting)}

• Nov 29, 2020. Add ESRGAN and DFDNet colab demo.
• Sep 8, 2020. Add blind face restoration inference codes: DFDNet.
• Aug 27, 2020. Add StyleGAN2 training and testing codes: StyleGAN2.

We provides simple pipelines to train/test/inference models for quick start. These pipelines/commands cannot cover all the cases and more details are in the following sections.

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.3
• NVIDIA GPU + CUDA

1. Clone repo

   git clone https://github.com/xinntao/BasicSR.git

2. Install dependent packages

   cd BasicSR
   pip install -r requirements.txt

3. Install BasicSR

   Please run the following commands in the BasicSR root path to install BasicSR:
   (Make sure that your GCC version: gcc >= 5)
   If you do not need the cuda extensions:
    dcn for EDVR
    upfirdn2d and fused_act for StyleGAN2
   please add --no_cuda_ext when installing

   python setup.py develop --no_cuda_ext

   If you use the EDVR and StyleGAN2 model, the above cuda extensions are necessary.

   python setup.py develop

   You may also want to specify the CUDA paths:

   CUDA_HOME=/usr/local/cuda \
   CUDNN_INCLUDE_DIR=/usr/local/cuda \
   CUDNN_LIB_DIR=/usr/local/cuda \
   python setup.py develop

Note that BasicSR is only tested in Ubuntu, and may be not suitable for Windows. You may try Windows WSL with CUDA supports :-) (It is now only available for insider build with Fast ring).

Please see project boards.

• Please refer to DatasetPreparation.md for more details.
• The descriptions of currently supported datasets (torch.utils.data.Dataset classes) are in Datasets.md.

• Training and testing commands: Please see TrainTest.md for the basic usage.
• Options/Configs: Please refer to Config.md.
• Logging: Please refer to Logging.md.

• The descriptions of currently supported models are in Models.md.
• Pre-trained models and log examples are available in ModelZoo.md.
• We also provide training curves in wandb:

Please see DesignConvention.md for the designs and conventions of the BasicSR codebase.
The figure below shows the overall framework. More descriptions for each component:
Datasets.md | Models.md | Config.md | Logging.md

This project is released under the Apache 2.0 license.
More details about license and acknowledgement are in LICENSE.

If BasicSR helps your research or work, please consider citing BasicSR.
The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@misc{wang2020basicsr,
  author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
                  Chao Dong and Chen Change Loy},
  title =        {BasicSR},
  howpublished = {\url{https://github.com/xinntao/BasicSR}},
  year =         {2020}
}

Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR. https://github.com/xinntao/BasicSR, 2020.

If you have any question, please email xintao.wang@outlook.com.