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 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 :)
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
realsr-ncnn-vulkan.exe -i input.jpg -o output.png -s 4
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-path realsr model path (default=models-DF2K_JPEG)
-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
andoutput-path
accept either file path or directory pathscale
= scale level, 4 = upscale 4xtile-size
= tile size, use smaller value to reduce GPU memory usage, default selects automaticallyload:proc:save
= thread count for the three stages (image decoding + realsr 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
- 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/realsr-ncnn-vulkan.git
cd realsr-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 400% output.png
srmd-ncnn-vulkan.exe -i origin.jpg -o 4x.png -s 4 -n -1
realsr-ncnn-vulkan.exe -i origin.jpg -o output.png -s 4 -x -m models-DF2K
- 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