nihui / Waifu2x Ncnn Vulkan
Licence: mit
waifu2x converter ncnn version, runs fast on intel / amd / nvidia GPU with vulkan
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waifu2x ncnn Vulkan
ncnn implementation of waifu2x converter. Runs fast on Intel / AMD / Nvidia with Vulkan API.
waifu2x-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/waifu2x-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
waifu2x-ncnn-vulkan.exe -i input.jpg -o output.png -n 2 -s 2
Full Usages
Usage: waifu2x-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=0)
-s scale upscale ratio (1/2/4/8/16/32, default=2)
-t tile-size tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
-m model-path waifu2x model path (default=models-cunet)
-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 path -
noise-level
= noise level, large value means strong denoise effect, -1 = no effect -
scale
= scale level, 1 = no scaling, 2 = upscale 2x -
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
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/waifu2x-ncnn-vulkan.git
cd waifu2x-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
Speed Comparison with waifu2x-caffe-cui
Environment
- Windows 10 1809
- AMD R7-1700
- Nvidia GTX-1070
- Nvidia driver 419.67
- CUDA 10.1.105
- cuDNN 10.1
Measure-Command { waifu2x-ncnn-vulkan.exe -i input.png -o output.png -n 2 -s 2 -t [block size] -m [model dir] }
Measure-Command { waifu2x-caffe-cui.exe -t 0 --gpu 0 -b 1 -c [block size] -p cudnn --model_dir [model dir] -s 2 -n 2 -m noise_scale -i input.png -o output.png }
cunet
Image Size | Target Size | Block Size | Total Time(s) | GPU Memory(MB) | |
---|---|---|---|---|---|
waifu2x-ncnn-vulkan | 200x200 | 400x400 | 400/200/100 | 0.86/0.86/0.82 | 638/638/197 |
waifu2x-caffe-cui | 200x200 | 400x400 | 400/200/100 | 2.54/2.39/2.36 | 3017/936/843 |
waifu2x-ncnn-vulkan | 400x400 | 800x800 | 400/200/100 | 1.17/1.04/1.02 | 2430/638/197 |
waifu2x-caffe-cui | 400x400 | 800x800 | 400/200/100 | 2.91/2.43/2.7 | 3202/1389/1178 |
waifu2x-ncnn-vulkan | 1000x1000 | 2000x2000 | 400/200/100 | 2.35/2.26/2.46 | 2430/638/197 |
waifu2x-caffe-cui | 1000x1000 | 2000x2000 | 400/200/100 | 4.04/3.79/4.35 | 3258/1582/1175 |
waifu2x-ncnn-vulkan | 2000x2000 | 4000x4000 | 400/200/100 | 6.46/6.59/7.49 | 2430/686/213 |
waifu2x-caffe-cui | 2000x2000 | 4000x4000 | 400/200/100 | 7.01/7.54/10.11 | 3258/1499/1200 |
waifu2x-ncnn-vulkan | 4000x4000 | 8000x8000 | 400/200/100 | 22.78/23.78/27.61 | 2448/654/213 |
waifu2x-caffe-cui | 4000x4000 | 8000x8000 | 400/200/100 | 18.45/21.85/31.82 | 3325/1652/1236 |
upconv_7_anime_style_art_rgb
Image Size | Target Size | Block Size | Total Time(s) | GPU Memory(MB) | |
---|---|---|---|---|---|
waifu2x-ncnn-vulkan | 200x200 | 400x400 | 400/200/100 | 0.74/0.75/0.72 | 482/482/142 |
waifu2x-caffe-cui | 200x200 | 400x400 | 400/200/100 | 2.04/1.99/1.99 | 995/546/459 |
waifu2x-ncnn-vulkan | 400x400 | 800x800 | 400/200/100 | 0.95/0.83/0.81 | 1762/482/142 |
waifu2x-caffe-cui | 400x400 | 800x800 | 400/200/100 | 2.08/2.12/2.11 | 995/546/459 |
waifu2x-ncnn-vulkan | 1000x1000 | 2000x2000 | 400/200/100 | 1.52/1.41/1.44 | 1778/482/142 |
waifu2x-caffe-cui | 1000x1000 | 2000x2000 | 400/200/100 | 2.72/2.60/2.68 | 1015/570/459 |
waifu2x-ncnn-vulkan | 2000x2000 | 4000x4000 | 400/200/100 | 3.45/3.42/3.63 | 1778/482/142 |
waifu2x-caffe-cui | 2000x2000 | 4000x4000 | 400/200/100 | 3.90/4.01/4.35 | 1015/521/462 |
waifu2x-ncnn-vulkan | 4000x4000 | 8000x8000 | 400/200/100 | 11.16/11.29/12.07 | 1796/498/158 |
waifu2x-caffe-cui | 4000x4000 | 8000x8000 | 400/200/100 | 9.24/9.81/11.16 | 995/546/436 |
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 waifu2x noise=2 scale=2
waifu2x-ncnn-vulkan.exe -i origin.jpg -o output.png -n 2 -s 2
Original waifu2x 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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