🚀 BasicSR

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Google Colab: GitHub Link | Google Drive Link
⏬ Google Drive: Pretrained Models | Reproduced Experiments ⏬ 百度网盘: 预训练模型 | 复现实验
📈 Training curves in wandb
💻 Commands for training and testing
⚡ HOWTOs

BasicSR is an open source image and video super-resolution toolbox based on PyTorch (will extend to more restoration tasks in the future).
_{(ESRGAN, EDVR, DNI, SFTGAN)}

✨ New Feature

Sep 8, 2020. Add blind face restoration inference codes: DFDNet. Note that it is slightly different from the official testing codes.

ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries
Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang
Aug 27, 2020. Add StyleGAN2 training and testing codes: StyleGAN2.

CVPR20: Analyzing and Improving the Image Quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila

Aug 19, 2020. A brand-new BasicSR v1.0.0 online.

⚡ HOWTOs

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.

🔧 Dependencies and Installation

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

Clone repo

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

Install dependent packages

cd BasicSR
pip install -r requirements.txt

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
```

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).

⏳ TODO List

Please see project boards.

🐢 Dataset Preparation

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

💻 Train and Test

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.

🏰 Model Zoo and Baselines

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:

📝 Codebase Designs and Conventions

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

📜 License and Acknowledgement

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

📧 Contact

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

runze-wang-sjtu/BasicSR