❗ ❗ ❗ This software is in the early development stage, it may bite your cat

ncnn implementation of Real-CUGAN converter. Runs fast on Intel / AMD / Nvidia / Apple-Silicon with Vulkan API.

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

Download Windows/Linux/MacOS Executable for Intel/AMD/Nvidia/Apple-Silicon GPU

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

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

Real-CUGAN (Real Cascade U-Nets for Anime Image Super Resolution)

https://github.com/bilibili/ailab/tree/main/Real-CUGAN

realcugan-ncnn-vulkan.exe -i input.jpg -o output.png

Usage: realcugan-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
  -n noise-level       denoise level (-1/0/1/2/3, default=-1)
  -s scale             upscale ratio (1/2/3/4, default=2)
  -t tile-size         tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
  -c syncgap-mode      sync gap mode (0/1/2/3, default=3)
  -m model-path        realcugan model path (default=models-se)
  -g gpu-id            gpu device to use (-1=cpu, 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)

• input-path and output-path accept either file path or directory path
• noise-level = noise level, large value means strong denoise effect, -1 = no effect
• scale = scale level, 1 = no scaling, 2 = upscale 2x
• tile-size = tile size, use smaller value to reduce GPU memory usage, default selects automatically
• syncgap-mode = sync gap mode, 0 = no sync, 1 = accurate sync, 2 = rough sync, 3 = very rough sync
• load:proc:save = thread count for the three stages (image decoding realcugan 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 a crash or error, try upgrading 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

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/realcugan-ncnn-vulkan.git
cd realcugan-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

convert origin.jpg -resize 200% output.png

convert origin.jpg -filter Lanczos -resize 200% output.png

realcugan-ncnn-vulkan.exe -i origin.jpg -o output.png -s 2 -n 1 -x

• https://github.com/bilibili/ailab/tree/main/Real-CUGAN

• 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