# Real-ESRGAN
[](https://github.com/xinntao/Real-ESRGAN/releases)
[](https://pypi.org/project/realesrgan/)
[](https://github.com/xinntao/Real-ESRGAN/issues)
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[](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN 
.
2. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#Portable-executable-files).
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](CONTRIBUTING.md). All contributors are list [here](CONTRIBUTING.md#Contributors).
:question: Frequently Asked Questions can be found in [FAQ.md](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 size. More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [anime_model.md](docs/anime_model.md)
- :white_check_mark: Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
- :white_check_mark: Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN). Thanks [@AK391](https://github.com/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](inference_realesrgan.py) 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](Training.md).
---
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](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration
:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An ppen-source image and video restoration toolbox
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.
:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison.
---
### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
> [[Paper](https://arxiv.org/abs/2107.10833)] [Project Page] [Demo]
> [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)
> 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 :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
- [x] 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.
---
### Portable executable files
You can download [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-macos.zip) **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):
```bash
./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 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](https://github.com/Tencent/ncnn) and [realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan) by [nihui](https://github.com/nihui).
---
## :wrench: Dependencies and Installation
- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 1.7](https://pytorch.org/)
### Installation
1. Clone repo
```bash
git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN
```
1. Install dependent packages
```bash
# 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
Download pre-trained models: [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
Download pretrained models:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
```
Inference!
```bash
python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs --face_enhance
```
Results are in the `results` folder
## :european_castle: Model Zoo
- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
- [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
- [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
- [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth)
- [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
- [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)
## :computer: Training and Finetuning on your own dataset
A detailed guide can be found in [Training.md](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`.