SRMD ncnn Vulkan

ncnn implementation of SRMD super resolution.

srmd-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/srmd-ncnn-vulkan/releases

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

Usages

Example Command

srmd-ncnn-vulkan.exe -i input.jpg -o output.png -n 3 -s 2

Full Usages

Usage: srmd-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 (-1/0/1/2/3/4/5/6/7/8/9/10, default=3)
  -s scale             upscale ratio (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        srmd model path (default=models-srmd)
  -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 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, 2 = upscale 2x, 3 = upscale 3x, 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 + waifu2x 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

Sample Images

Original Image

Upscale 4x with ImageMagick Lanczo4 Filter

convert origin.jpg -resize 400% output.png

Upscale 4x with waifu2x scale=2 model=upconv_7_photo twice

waifu2x-ncnn-vulkan.exe -i origin.jpg -o 2x.png -s 2 -m models-upconv_7_photo
waifu2x-ncnn-vulkan.exe -i 2x.png -o 4x.png -s 2 -m models-upconv_7_photo

Upscale 4x with srmd noise=3 scale=4

srmd-ncnn-vulkan.exe -i origin.jpg -o output.png -n 3 -s 4

Original SRMD Project

• https://github.com/cszn/SRMD
• https://github.com/cszn/KAIR

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