GitHub - nihui/realcugan-ncnn-vulkan: real-cugan converter ncnn version, runs fa...
source link: https://github.com/nihui/realcugan-ncnn-vulkan
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Real-CUGAN ncnn Vulkan
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
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 :)
About Real-CUGAN
Real-CUGAN (Real Cascade U-Nets for Anime Image Super Resolution)
https://github.com/bilibili/ailab/tree/main/Real-CUGAN
Usages
Example Command
realcugan-ncnn-vulkan.exe -i input.jpg -o output.png
Full Usages
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
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 automaticallysyncgap-mode
= sync gap mode, 0 = no sync, 1 = accurate sync, 2 = rough sync, 3 = very rough syncload: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:
Build from Source
- 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
Sample Images
Original Image
Upscale 2x with ImageMagick
convert origin.jpg -resize 200% output.png
Upscale 2x with ImageMagick Lanczo4 Filter
convert origin.jpg -filter Lanczos -resize 200% output.png
Upscale 2x with Real-CUGAN
realcugan-ncnn-vulkan.exe -i origin.jpg -o output.png -s 2 -n 1 -x
Original Real-CUGAN Project
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
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