flowframes/README.md

# Flowframes - Windows GUI for Video Interpolation
Flowframes Windows GUI for video interpolation - Supports RIFE, RIFE-NCNN, DAIN-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](https://nmkd.itch.io/flowframes) or, for the most recent beta versions, on [Patreon](https://www.patreon.com/n00mkrad). This repo does not provide builds.
* Run Flowframes.exe
* Pre-1.18: Select the components you want to install (certain packages are required, cannot be unticked)

Starting with 1.18, the installer has been removed, and Flowframes is instead distributed as an all-in-one archive. Download the "Full" file if you are using a Maxwell/Pascal/Turing GPU and want to use embedded Pytorch. Use "NoPython" if you run an AMD GPU or want to use your system Python/Pytorch installation.



## Using A Pytorch Implementation

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.

[More Details On Python Dependencies](PythonDependencies.md)



## Running A Pytorch Implementation on Nvidia Ampere 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.
* Auto-Encode: Encode video while interpolating. Optionally delete the already encoded frames to minimize disk space usage.
* Save Output Frames As JPEG: Save interpolated frames as JPEG before encoding. Not recommended unless you have little disk space.

### AI Specific Settings

* RIFE - UHD Mode - This mode changes some scaling parameters and should improve results on high-resolution video.
* 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.