nihui / Srmd Ncnn Vulkan
Licence: mit
SRMD super resolution implemented with ncnn library
Stars: ✭ 186
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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
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, 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
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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