Go to file
N00MKRAD 320b21f29e added funding file 2020-12-07 16:20:00 +01:00
Code added funding file 2020-12-07 16:20:00 +01:00
Media Initial 2020-11-23 16:51:05 +01:00
Pkgs Updated rife-ncnn-vulkan (speed improvement, no tiling) 2020-12-04 23:45:04 +01:00
.gitignore Initial commit 2020-11-23 14:51:13 +01:00
FUNDING.yml added funding file 2020-12-07 16:20:00 +01:00
LICENSE Initial commit 2020-11-23 14:51:13 +01:00
README.md Update README.md 2020-12-07 00:48:08 +01:00

README.md

Flowframes - Windows GUI for Video Interpolation

Flowframes Windows GUI for video interpolation - Supports RIFE, RIFE-NCNN, DAIN-NCNN, CAIN-NCNN networks.

Flowframes is open-source donationware. Builds are released for free on itch after an early-access period on Patreon. This repo's code is complete and does not "paywall" experienced users who want to compile the program themselves.

However, I do not provide support for self-built versions as I can't guarantee that the code of this repo is stable at any given moment.

Installation

  • Download on itch or, for the most recent beta versions, on Patreon. This repo does not provide builds.
  • Run Flowframes.exe
  • Select the components you want to install (certain packages are required, cannot be unticked)

Using A Pytorch AI

Some of the AI networks run on Tencent's NCNN framework, which allows them to run on any modern (Vulkan-capable) GPU.

However, others (like RIFE) run best via their original Pytorch implementation.

The requirements to run these are the following:

  • A modern Nvidia GPU (750 Ti, 900/1000/1600/2000/3000 Series).
  • A Python installation including Pytorch (1.5 or later) as well as the packages opencv-python and imageio.
    • You can install a portable version of all those requirements from the Flowframes Installer. This does not support RTX 3000 cards yet.

Running A Pytorch AI on Nvidia Ampere (RTX 3000) GPUs

I do not have an Ampere card yet, so I can't fully test Flowframes on an RTX 3000 series GPU.

However, users have reported that you can run RIFE on Ampere cards by installing a recent Pytorch 1.7 build. CUDA 11 is also required, but you should already have it if you use up-to-date GPU drivers. NCNN-based AIs should work out of the box.

Configuration

All Settings have reasonable defaults, so users do not need to do any configuration before using the program.

Here is an explanation of some of the more important settings.

General

  • Maximum Video Size: Frames are exported at this resolution if the video is larger. Lower resolutions speed up interpolation a lot.

Interpolation

  • Copy Audio: Audio will be saved to a separate file when extracting the frames and will afterwards be merged into the output.
    • Not guaranteed to work with all audio codecs. Supported are: M4A/AAC, Vorbis, Opus, MP2, PCM/Raw.
  • Remove Duplicate Frames: This is meant for 2D animation. Removing duplicates makes a smooth interpolation possible.
    • You can disable this completely if you only use content without duplicates (e.g. camera footage, CG renders).
  • Animation Loop: This will make looped animations interpolate to a perfect loop by copying the first frame to the end of the frames.
  • Don't Interpolate Scene Changes: This avoids interpolating scene changes (cuts) as this would produce weird a morphing effect.
  • Save Output Frames As JPEG: Save interpolated frames as JPEG before encoding. Not recommended unless you have little disk space.

AI Specific Settings

  • RIFE - Use Fast Parallel Mode - Speeds up RIFE interpolation a lot if you have lots of VRAM. Not recommended with <8GB GPUs.
  • GPU IDs: 0 is the default for setups with one dedicated GPU. Four dedicated GPUs would mean 0,1,2,3 for example.
  • NCNN Processing Threads: Increasing this number to 2, 3 or 4 can improve GPU utilization, but also slow things down.

Video Export

  • Encoding Options: Set options for video/GIF encoding. Refer to the FFmpeg and Gifski documentations.
  • Minimum Video Length: Make sure the output is as long as this value by looping it.
  • Maximum Output Frame Rate: Limit frame rate, for example, if you want a 60 FPS output from a 24 FPS video.

Debugging / Experimental

  • Show Hidden CMD Windows: This will show the windows for AI processes. Can be useful for debugging.
  • FFprobe: Count Frames Manually: This uses a slower way of getting the input video's total frame count, but works reliably.