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January 4, 2021 23:16
November 24, 2020 23:28
November 24, 2020 09:09

RIFE ncnn Vulkan

CI download

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 Windows/Linux/MacOS Executable for Intel/AMD/Nvidia GPU

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)

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


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

Usage: rife-ncnn-vulkan -0 infile -1 infile1 -o outfile [options]...
       rife-ncnn-vulkan -i indir -o outdir [options]...

  -h                   show this help
  -v                   verbose output
  -0 input0-path       input image0 path (jpg/png/webp)
  -1 input1-path       input image1 path (jpg/png/webp)
  -i input-path        input image directory (jpg/png/webp)
  -o output-path       output image path (jpg/png/webp) or directory
  -n num-frame         target frame count (default=N*2)
  -s time-step         time step (0~1, default=0.5)
  -m model-path        rife model path (default=rife-v2.3)
  -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 spatial tta mode
  -z                   enable temporal tta mode
  -u                   enable UHD mode
  -f pattern-format    output image filename pattern format (%08d.jpg/png/webp, default=ext/%08d.png)
  • 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:

Build from Source

  1. Download and setup the Vulkan SDK from
  • 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
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 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

origin0 origin1

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

Other Open-Source Code Used


RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation implemented with ncnn library








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