SPAN-ncnn-vulkan

Custom models:

ClearRealityV1 (4X) by Kim2091
SPANkendata (4X) by terrainer
2x-span-anime-pretrain (2X) by Kim2091
2xHFA2kSpan (2x) by Phhofm
4x-Nomos8k-span-otf-strong (4x) by Helaman
4x-Nomos8k-span-otf-medium (4x) by Helaman
4x-Nomos8k-span-otf-weak (4x) by Helaman

ncnn implementation of SPAN, Swift Parameter-free Attention Network for Efficient Super-Resolution

span-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/tntwise/span-ncnn-vulkan/releases

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

About SPAN

SPAN (Swift Parameter-free Attention Network for Efficient Super-Resolution)

https://github.com/hongyuanyu/SPAN

Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zu

Usages

Input one image, output one upscaled frame image.

Example Commands

./span-ncnn-vulkan -m models/ -n spanx4_ch48 -s 4 -i 0.jpg  -o 01.jpg
./span-ncnn-vulkan -m models/ -n spanx4_ch48 -s 4 -i input_frames/ -o output_frames/

Example below runs on CPU, Discrete GPU, and Integrated GPU all at the same time. Uses 2 threads for image decoding, 4 threads for one CPU worker, 4 threads for another CPU worker, 2 threads for discrete GPU, 1 thread for integrated GPU, and 4 threads for image encoding.

./span-ncnn-vulkan -m models/ -n spanx4_ch48 -s 4 -i input_frames/ -o output_frames/ -g -1,-1,0,1 -j 2:4,4,2,1:4

Video Upscaling with FFmpeg

mkdir input_frames
mkdir output_frames

# find the source fps and format with ffprobe, for example 24fps, AAC
ffprobe input.mp4

# extract audio
ffmpeg -i input.mp4 -vn -acodec copy audio.m4a

# decode all frames
ffmpeg -i input.mp4 input_frames/frame_%08d.png

# upscale 4x resolution
./span-ncnn-vulkan -m models/ -n spanx4_ch48 -s 4 -i input_frames -o output_frames

# encode interpolated frames in 48fps with audio
ffmpeg -framerate 24 -i output_frames/%08d.png -i audio.m4a -c:a copy -crf 20 -c:v libx264 -pix_fmt yuv420p output.mp4

Full Usages

Usage: span-ncnn-vulkan -i infile -o outfile [options]...

  -h                   show this help
  -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 (can be 2, 3, 4. default=4)
  -t tile-size         tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
  -m model-path        folder path to the pre-trained models. default=models
  -n model-name        model name (default=spanx4_ch48, can be spanx4_ch48 | spanx2_ch52 | spanx4_ch48 | spanx4_ch52)
  -g gpu-id            gpu device to use (default=auto) can be 0,1,2 for multi-gpu
  -c cpu-only          use only CPU for upscaling, instead of vulkan
  -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)
  -v                   verbose output

input-path and output-path accept file directory
load:proc:save = thread count for the three stages (image decoding + 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.
pattern-format = the filename pattern and format of the image to be output, png is better supported, however webp generally yields smaller file sizes, both are losslessly encoded
scale = upscale multiplier, must match model.

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/tntwise/span-ncnn-vulkan.git
cd span-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

Model

model	upstream version
spanx2_ch48	spanx2_ch48
spanx2_ch52	spanx2_ch52
spanx4_ch48	spanx4_ch48
spanx4_ch52	spanx4_ch52

Sample Images

Original Image

Upscale 4X with spanx4_ch48 model

./span-ncnn-vulkan -m models/ -n spanx4_ch48 -s 4 -i 0.png -o out.png

Upscale 2X with 2xHFA2kSPAN model

./span-ncnn-vulkan -m custom_models/ -n 2xHFA2kSPAN_27k -s 2 -i 0.png -o 2xHFA2kSPAN.png

Original SPAN Project

https://github.com/hongyuanyu/SPAN

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

TNTwise / SPAN-ncnn-vulkan