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Real-ESRGAN: Blind Super-Resolution with Pure Synthetic Data

Demos Videos

The.Legend.Of.Nezha_X4_compare.MP4
standard-test_outx2.mp4
onepiece_demo_outx4.mp4

YouTube

Dependencies and Installation

  • Python >= 3.7
  • PyTorch >= 1.7

Installation

  1. Clone repo

    git clone https://github.com/saba99/Video-Super-Resolution-ESRGAN.git
cd Video-Super-Resolution-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

There are usually three ways to inference Real-ESRGAN.

  1. Online inference
  2. Portable executable files (NCNN)
  3. Python script

Online inference

  1. Colab Demo for Real-ESRGAN | Colab Demo for Real-ESRGAN (anime videos).

Portable executable files (NCNN)

./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n model_name

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 portable 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
  -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 (can be 2, 3, 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 the pre-trained models. default=models
  -n model-name        model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
  -g gpu-id            gpu device to use (default=auto) 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)
  -v                   verbose output

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.

Python script

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 --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
  --fp32               Use fp32 precision during inference. Default: fp16 (half precision).
  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto