❗ ❗ ❗ 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 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 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
-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
andoutput-path
accept either file path or directory pathnoise-level
= noise level, large value means strong denoise effect, -1 = no effectscale
= scale level, 1 = no scaling, 2 = upscale 2xtile-size
= tile size, use smaller value to reduce GPU memory usage, default selects automaticallyload: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
- 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
- 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
- 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/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