- Colab Demo for Real-ESRGAN
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- Portable Windows / Linux / MacOS executable files for Intel/AMD/Nvidia GPU. You can find more information here.
Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
🎨 Real-ESRGAN needs your contributions. Any contributions are welcome, such as new features/models/typo fixes/suggestions/maintenance, etc. See CONTRIBUTING.md. All contributors are list here.
❓ Frequently Asked Questions can be found in FAQ.md.
🚩 Updates
- ✅ Add RealESRGAN_x4plus_anime_6B.pth, which is optimized for anime images with much smaller model size. More details and comparisons with waifu2x are in anime_model.md
- ✅ Support finetuning on your own data or paired data (i.e., finetuning ESRGAN). See here
- ✅ Integrate GFPGAN to support face enhancement.
- ✅ Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo. Thanks @AK391
- ✅ Support arbitrary scale with
--outscale(It actually further resizes outputs withLANCZOS4). Add RealESRGAN_x2plus.pth model. - ✅ The inference code supports: 1) tile options; 2) images with alpha channel; 3) gray images; 4) 16-bit images.
- ✅ The training codes have been released. A detailed guide can be found in Training.md.
If Real-ESRGAN is helpful in your photos/projects, please help to ⭐ this repo or recommend it to your friends. Thanks😊
Other recommended projects:
[Paper] [Project Page] [Demo]
Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan
Applied Research Center (ARC), Tencent PCG
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
We have provided a pretrained model (RealESRGAN_x4plus.pth) with upsampling X4.
Note that RealESRGAN may still fail in some cases as the real-world degradations are really too complex.
Moreover, it may not perform well on human faces, text, etc, which will be optimized later.
Real-ESRGAN will be a long-term supported project (in my current plan 😃). It will be continuously updated in my spare time.
Here is a TODO list in the near future:
- optimize for human faces
- optimize for texts
- optimize for anime images
- support more scales
- support controllable restoration strength
If you have any good ideas or demands, please open an issue/discussion to let me know.
If you have some images that Real-ESRGAN could not well restored, please also open an issue/discussion. I will record it (but I cannot guarantee to resolve it:stuck_out_tongue:). If necessary, I will open a page to specially record these real-world cases that need to be solved, but the current technology is difficult to handle well.
You can download Windows / Linux / MacOS executable files for Intel/AMD/Nvidia GPU.
This executable file is portable and includes all the binaries and models required. No CUDA or PyTorch environment is needed.
You can simply run the following command (the Windows example, more information is in the README.md of each executable files):
./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.pngWe have provided three models:
- realesrgan-x4plus (default)
- realesrnet-x4plus
- realesrgan-x4plus-anime (optimized for anime images, small model size)
You can use the -n argument for other models, for example, ./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus
Note that it may introduce block inconsistency (and also generate slightly different results from the PyTorch implementation), because this executable file first crops the input image into several tiles, and then processes them separately, finally stitches together.
This executable file is based on the wonderful Tencent/ncnn and realsr-ncnn-vulkan by nihui.
- Python >= 3.7 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 1.7
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Clone repo
git clone https://github.com/xinntao/Real-ESRGAN.git cd Real-ESRGAN -
Install dependent packages
# Install basicsr - https://github.com/xinntao/BasicSR # We use BasicSR for both training and inference pip install basicsr # facexlib and gfpgan are for face enhancement pip install facexlib pip install gfpgan pip install -r requirements.txt python setup.py develop
Download pre-trained models: RealESRGAN_x4plus.pth
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_modelsInference!
python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs --face_enhanceResults are in the results folder
Pre-trained models: RealESRGAN_x4plus_anime_6B
More details and comparisons with waifu2x are in anime_model.md
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P experiments/pretrained_models
# inference
python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth --input inputsResults are in the results folder
- RealESRGAN_x4plus
- RealESRGAN_x4plus_netD
- RealESRGAN_x4plus_anime_6B
- RealESRGAN_x4plus_anime_6B_netD
- RealESRNet_x4plus
- RealESRGAN_x2plus
- RealESRGAN_x2plus_netD
- official ESRGAN_x4
A detailed guide can be found in Training.md.
@Article{wang2021realesrgan,
title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
author={Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
journal={arXiv:2107.10833},
year={2021}
}
If you have any question, please email xintao.wang@outlook.com or xintaowang@tencent.com.