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📖 Real-ESRGAN .

[Paper]   [YouTube Video]  [PPT slides]

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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

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⚡ Quick Inference

Online inference

1. You can try in our website: ARC Demo (RealESRGAN_x4plus_anime_6B)
2. Online Replicate demo:
3. Online Colab demo for Real-ESRGAN:
4. Online Colab demo for for Real-ESRGAN (anime videos):

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⚡ Python script

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 --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' 
         (Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | realesr-animevideov3 | realesr-general-x4v3) 
  -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 
  --fp32                      Use fp32 precision during inference. Default: '--fp32'
  --ext                       Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto 
  -dn, --denoise_strength     Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability, 'Only used for the realesr-general-x4v3 model' 
  --alpha_upsampler           The upsampler for the alpha channels. Options: realesrgan | bicubic . Default: 'realesrgan' 
  -g, --gpu-id                gpu device to use. Default=None. can be 0,1,2 for multi-gpu 

Inference general images

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Inference!

python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance

  The most straightforward way of improving model performance is to fine-tune on some specific datasets.

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Notes

• Add the realesr-general-x4v3 model - a tiny small model for general scenes. It also supports the -dn option to balance the noise (avoiding over-smooth results). -dn is short for denoising strength.
• 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.

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