Video & Motion

TokenFlow

Consistent Diffusion Features for Video Editing

Status
UnmaintainedNo commits in 568 days.
Last checked
Checked · Oct 13, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

TokenFlow is an official Pytorch implementation that provides consistent diffusion features for video editing. It's designed to enhance the quality and consistency of video editing tasks.

TokenFlow stands out as an open-source implementation of a cutting-edge model for consistent diffusion in video editing, offering researchers and developers a powerful tool to enhance their projects.

When it fits — and when it doesn’t

A good fit for

  • Teams working on research projects involving video diffusion models

  • Developers needing a consistent approach to video editing tasks

  • Individuals interested in implementing the latest AI techniques for video processing

Look elsewhere if

  • Projects requiring real-time video processing capabilities

  • Applications that need cloud-based deployment without local setup

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited integrations with other video editing tools and libraries

    Documentation only shows basic integration examples, lacks support for popular video processing frameworks like FFmpeg or OpenCV

  2. 02

    Performance issues on large video files

    Test runs on 4K videos showed significant slowdowns and memory usage spikes

What it does well

  • Consistent diffusion features for video editing

  • Implementation of TokenFlow model from ICLR 2024 paper

  • Open-source under MIT license

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up TokenFlow

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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