• Real-ESRGAN
• • • :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
    • Portable executable files
    • Usage of executable files
  • :wrench: Dependencies and Installation
  • • Installation
  • :zap: Quick Inference
  • • Inference general images
    • Inference anime images
    • Usage of python script
  • :european_castle: Model Zoo
  • :computer: Training and Finetuning on your own dataset
  • BibTeX
  • :e-mail: Contact
  • :hugs: Acknowledgement

Real-ESRGAN

English | 简体中文

🔥 🔥 🔥 Add small video models for anime videos (针对动漫视频的小模型). Please see anime video models.

1. Colab Demo for Real-ESRGAN .
2. Colab Demo for Real-ESRGAN (anime videos) .
3. Portable Windows / Linux / MacOS executable files for Intel/AMD/Nvidia GPU. You can find more information here. The ncnn implementation is in Real-ESRGAN-ncnn-vulkan.

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 (Well, it is still empty there =-=||).

🌌 Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in feedback.md.

🚩 Updates

• ✅ Add small models for anime videos. More details are in anime video models.
• ✅ Add the ncnn implementation Real-ESRGAN-ncnn-vulkan.
• ✅ 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 with LANCZOS4). 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:

▶️ GFPGAN: A practical algorithm for real-world face restoration

▶️ BasicSR: An open-source image and video restoration toolbox

▶️ facexlib: A collection that provides useful face-relation functions.

▶️ HandyView: A PyQt5-based image viewer that is handy for view and comparison.

---

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

[Paper]   [Project Page]   [YouTube Video]   [B站讲解]   [Poster]   [PPT slides]

Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan

Tencent ARC Lab; 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😛). 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.

---

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 -n model_name

We have provided five models:

1. realesrgan-x4plus (default)
2. realesrnet-x4plus
3. realesrgan-x4plus-anime (optimized for anime images, small model size)
4. RealESRGANv2-animevideo-xsx2 (anime video, X2)
5. RealESRGANv2-animevideo-xsx4 (anime video, X4)

You can use the -n argument for other models, for example, ./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus

Usage of executable files

1. Please refer to Real-ESRGAN-ncnn-vulkan for more details.
2. Note that it does not support all the functions (such as outscale) as the python script inference_realesrgan.py.

Usage: realesrgan-ncnn-vulkan.exe -i infile -o outfile [options]...

  -h                   show this help
  -v                   verbose output
  -i input-path        input image path (jpg/png/webp) or directory
  -o output-path       output image path (jpg/png/webp) or directory
  -s scale             upscale ratio (4, default=4)
  -t tile-size         tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
  -m model-path        folder path to pre-trained models(default=models)
  -n model-name        model name (default=realesrgan-x4plus, can be realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
  -g gpu-id            gpu device to use (default=0) can be 0,1,2 for multi-gpu
  -j load:proc:save    thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
  -x                   enable tta mode
  -f format            output image format (jpg/png/webp, default=ext/png)

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.

---

🔧 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
   

⚡ 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 -n RealESRGAN_x4plus -i 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 -n RealESRGAN_x4plus_anime_6B -i inputs

Results are in the results folder

Usage of python script

1. You can use X4 model for arbitrary output size with the argument outscale. The program will further perform cheap resize operation after the Real-ESRGAN output.

Usage: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile -o outfile [options]...

A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --half --face_enhance

  -h                   show this help
  -i --input           Input image or folder. Default: inputs
  -o --output          Output folder. Default: results
  -n --model_name      Model name. Default: RealESRGAN_x4plus
  -s, --outscale       The final upsampling scale of the image. Default: 4
  --suffix             Suffix of the restored image. Default: out
  -t, --tile           Tile size, 0 for no tile during testing. Default: 0
  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
  --half               Whether to use half precision during inference. Default: False
  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto

🏰 Model Zoo

Please see docs/model_zoo.md

💻 Training and Finetuning on your own dataset

A detailed guide can be found in Training.md.

BibTeX

@InProceedings{wang2021realesrgan,
    author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
    title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
    booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
    date      = {2021}
}

📧 Contact

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

🤗 Acknowledgement

Thanks for all the contributors.

• AK391: Integrate RealESRGAN to Huggingface Spaces with Gradio. See Gradio Web Demo.
• Asiimoviet: Translate the README.md to Chinese (中文).
• 2ji3150: Thanks for the detailed and valuable feedbacks/suggestions.