RealSR ncnn Vulkan

ncnn implementation of Real-World Super-Resolution via Kernel Estimation and Noise Injection super resolution.

realsr-ncnn-vulkan uses ncnn project as the universal neural network inference framework.

Download

Download Windows/Linux/MacOS Executable for Intel/AMD/Nvidia GPU

https://github.com/nihui/realsr-ncnn-vulkan/releases

This package includes all the binaries and models required. It is portable, so no CUDA or Caffe runtime environment is needed :)

About RealSR

Real-World Super-Resolution via Kernel Estimation and Noise Injection (CVPRW 2020)

https://github.com/jixiaozhong/RealSR

Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang

Tencent YouTu Lab

Our solution is the winner of CVPR NTIRE 2020 Challenge on Real-World Super-Resolution in both tracks.

https://arxiv.org/abs/2005.01996

Usages

Example Command

realsr-ncnn-vulkan.exe -i input.jpg -o output.png -m a -s 4

Full Usages

Usage: realsr-ncnn-vulkan -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-id        realsr model id (default load settings from ./spv/0modelset.rsr   //a='models-DF2K_JPEG', b='models-DF2K', c='models-ESRGAN')
  -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)

• input-path and output-path accept either file path or directory path
• scale = scale level, 4 = upscale 4x
• tile-size = tile size, use smaller value to reduce GPU memory usage, default selects automatically
• load:proc:save = thread count for the three stages (image decoding + waifu2x upscaling + image encoding), using larger values may increase GPU usage and consume more GPU memory. You can tune this configuration with "4:4:4" for many small-size images, and "2:2:2" for large-size images. The default setting usually works fine for most situations. If you find that your GPU is hungry, try increasing thread count to achieve faster processing.
• format = the format of the image to be output, png is better supported, however webp generally yields smaller file sizes, both are losslessly encoded

If you encounter crash or error, try to upgrade your GPU driver

• Intel: https://downloadcenter.intel.com/product/80939/Graphics-Drivers
• AMD: https://www.amd.com/en/support
• NVIDIA: https://www.nvidia.com/Download/index.aspx

Build from Source

1. Download and setup the Vulkan SDK from https://vulkan.lunarg.com/

• For Linux distributions, you can either get the essential build requirements from package manager

dnf install vulkan-headers vulkan-loader-devel

apt-get install libvulkan-dev

pacman -S vulkan-headers vulkan-icd-loader

2. Clone this project with all submodules

git clone https://github.com/nihui/realsr-ncnn-vulkan.git
cd realsr-ncnn-vulkan
git submodule update --init --recursive

3. Build with CMake

• You can pass -DUSE_STATIC_MOLTENVK=ON option to avoid linking the vulkan loader library on MacOS

mkdir build
cd build
cmake ../src
cmake --build . -j 4

Sample Images

Original Image

Upscale 4x with ImageMagick Lanczo4 Filter

convert origin.jpg -resize 400% output.png

Upscale 4x with srmd scale=4 noise=-1

srmd-ncnn-vulkan.exe -i origin.jpg -o 4x.png -s 4 -n -1

Upscale 4x with realsr model=DF2K scale=4 tta=1

realsr-ncnn-vulkan.exe -i origin.jpg -o output.png -s 4 -x -m models-DF2K

Original RealSR Project

• https://github.com/jixiaozhong/RealSR

Other Open-Source Code Used

• https://github.com/Tencent/ncnn for fast neural network inference on ALL PLATFORMS
• https://github.com/webmproject/libwebp for encoding and decoding Webp images on ALL PLATFORMS
• https://github.com/nothings/stb for decoding and encoding image on Linux / MacOS
• https://github.com/tronkko/dirent for listing files in directory on Windows