rife-ncnn-vulkan

RIFE ncnn Vulkan

ncnn implementation of RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation.

rife-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/rife-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 RIFE

RIFE (Real-Time Intermediate Flow Estimation for Video Frame Interpolation)

https://github.com/hzwer/arXiv2020-RIFE

Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang

https://rife-vfi.github.io

https://arxiv.org/abs/2011.06294

Usages

Input two frame images, output one interpolated frame image.

Example Commands

./rife-ncnn-vulkan -0 0.jpg -1 1.jpg -o 01.jpg
./rife-ncnn-vulkan -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.

./rife-ncnn-vulkan -i input_frames/ -o output_frames/ -g -1,-1,0,1 -j 2:4,4,2,1:4

Video Interpolation 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

# interpolate 2x frame count
./rife-ncnn-vulkan -i input_frames -o output_frames

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

Full Usages

…

• input0-path, input1-path and output-path accept file path
• input-path and output-path accept file directory
• num-frame = target frame count
• time-step = interpolation time
• load:proc:save = thread count for the three stages (image decoding + rife interpolation + 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

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

1. 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

1. Clone this project with all submodules

git clone https://github.com/nihui/rife-ncnn-vulkan.git
cd rife-ncnn-vulkan
git submodule update --init --recursive

1. 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
rife	1.2
rife-HD	1.5
rife-UHD	1.6
rife-anime	1.8
rife-v2	2.0
rife-v2.3	2.3
rife-v2.4	2.4
rife-v3.0	3.0
rife-v3.1	3.1
rife-v4	4.0
rife-v4.6	4.6

Sample Images

Original Image

Interpolate with rife rife-anime model

rife-ncnn-vulkan.exe -m models/rife-anime -0 0.png -1 1.png -o out.png

Interpolate with rife rife-anime model + TTA-s

rife-ncnn-vulkan.exe -m models/rife-anime -x -0 0.png -1 1.png -o out.png

Original RIFE Project

• https://github.com/hzwer/arXiv2020-RIFE

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