Real-ESRGAN

Real-ESRGAN

1. Colab Demo for Real-ESRGAN .
2. 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.

:art: 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.

:question: Frequently Asked Questions can be found in FAQ.md.

:triangular_flag_on_post: Updates

• :white_check_mark: 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
• :white_check_mark: Support finetuning on your own data or paired data (i.e., finetuning ESRGAN). See here
• :white_check_mark: Integrate GFPGAN to support face enhancement.
• :white_check_mark: Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo. Thanks @AK391
• :white_check_mark: Support arbitrary scale with --outscale (It actually further resizes outputs with LANCZOS4). Add RealESRGAN_x2plus.pth model.
• :white_check_mark: The inference code supports: 1) tile options; 2) images with alpha channel; 3) gray images; 4) 16-bit images.
• :white_check_mark: The training codes have been released. A detailed guide can be found in Training.md.

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If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush:
Other recommended projects:
:arrow_forward: GFPGAN: A practical algorithm for real-world face restoration
:arrow_forward: BasicSR: An open-source image and video restoration toolbox
:arrow_forward: facexlib: A collection that provides useful face-relation functions.
:arrow_forward: HandyView: A PyQt5-based image viewer that is handy for view and comparison.

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:book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

[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

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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 :smiley:). 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.

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Portable executable files

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.png

We have provided three models:

1. realesrgan-x4plus (default)
2. realesrnet-x4plus
3. 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.

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:wrench: Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7

Installation

1. Clone repo

   git clone https://github.com/xinntao/Real-ESRGAN.git
   cd Real-ESRGAN
   

2. 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
   

:zap: Quick Inference

Inference general images

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_models

Inference!

python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs --face_enhance

Results are in the results folder

Inference anime images

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 inputs

Results are in the results folder

:european_castle: Model Zoo

• RealESRGAN_x4plus
• RealESRGAN_x4plus_netD
• RealESRGAN_x4plus_anime_6B
• RealESRGAN_x4plus_anime_6B_netD
• RealESRNet_x4plus
• RealESRGAN_x2plus
• RealESRGAN_x2plus_netD
• official ESRGAN_x4

:computer: Training and Finetuning on your own dataset

A detailed guide can be found in Training.md.

BibTeX

@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}
}

:e-mail: Contact

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