GitHub - nihui/realsr-ncnn-vulkan: RealSR super resolution implemented with ncnn library (original) (raw)

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 -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-path realsr model path (default=models-DF2K_JPEG) -g gpu-id gpu device to use (-1=cpu, 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 + 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

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/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

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