- 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. The ncnn implementation is in Real-ESRGAN-ncnn-vulkan.
感谢大家的关注和使用:-) 关于动漫插画的模型，目前还有很多问题，主要有: 1. 视频处理不了; 2. 景深虚化有问题; 3. 不可调节, 效果过了; 4. 改变原来的风格。大家提供了很好的反馈。我会逐步整理这些反馈，更新在 这个文档。希望不久之后，有新模型可以使用
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 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 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] [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: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
- Please refer to Real-ESRGAN-ncnn-vulkan for more details.
- Note that it does not support all the functions (such as
outscale) as the python scriptinference_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.
- 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
- 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 --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --output outfile [options]...
A common command: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --netscale 4 --outscale 3.5 --half --face_enhance
-h show this help
--input Input image or folder. Default: inputs
--output Output folder. Default: results
--model_path Path to the pre-trained model. Default: experiments/pretrained_models/RealESRGAN_x4plus.pth
--netscale Upsample scale factor of the network. Default: 4
--outscale The final upsampling scale of the image. Default: 4
--suffix Suffix of the restored image. Default: out
--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-
RealESRGAN_x4plus: X4 model for general images
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RealESRGAN_x4plus_anime_6B: Optimized for anime images; 6 RRDB blocks (slightly smaller network)
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RealESRGAN_x2plus: X2 model for general images
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RealESRNet_x4plus: X4 model with MSE loss (over-smooth effects)
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official ESRGAN_x4: official ESRGAN model (X4)
The following models are discriminators, which are usually used for fine-tuning.
A detailed guide can be found in Training.md.
@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}
}
If you have any question, please email xintao.wang@outlook.com or xintaowang@tencent.com.