# omerbt/TokenFlow

Official Pytorch Implementation for "TokenFlow: Consistent Diffusion Features for Consistent Video Editing" presenting "TokenFlow" (ICLR 2024)

Repository: https://github.com/omerbt/TokenFlow
Canonical: https://ross.abutalabs.com/products/tokenflow
Homepage: https://diffusion-tokenflow.github.io
Language: Python
License: MIT
License Family: permissive
Topics: stable-diffusion, text-to-image, text-to-video, video-editing, tokenflow, iclr2024
Last push: 2025-02-03T15:34:18+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 4, release rhythm 35, longevity 81
- inputs: {"age_days": 1140, "days_push": 576, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1708, forks 143 (observed 2026-08-28T04:05:25.503851+00:00)

## What it is
TokenFlow is the official PyTorch implementation of an ICLR 2024 paper for text-driven, temporally consistent video editing using a pre-trained text-to-image diffusion model. It propagates diffusion features across frames via inter-frame correspondences, requiring no training or fine-tuning.

## Use cases
- edit a video with a text prompt while preserving motion
- apply text-to-image editing methods consistently across video frames
- change the appearance of objects in a video without retraining a model
- research on diffusion feature consistency for video
- reproduce TokenFlow paper results

## When to choose
- you need training-free, temporally consistent video editing with Stable Diffusion
- you want a research-grade reference implementation of the TokenFlow method
- you already have a text-to-image editing method and want to extend it to video

## When to avoid
- you need real-time or production video editing at scale
- you lack a GPU or cannot run diffusion models locally
- you want to generate videos from scratch rather than edit existing footage

## Facets
- artifact type: library
- maturity: stable
- function: video-processing, machine-learning, image-processing
- domain: artificial-intelligence, computer-vision, deep-learning
- platform: python
- tags: stable-diffusion, diffusion-models, text-driven-editing, video-editing, research-code, iclr-2024, video, gpu

## Member repositories
- omerbt/TokenFlow (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:25.503851+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:01.651257+00:00, confidence not recorded.
  - readme: https://github.com/omerbt/TokenFlow (fetched 2026-08-28T04:05:25.503851+00:00, sha d2c7ae5e7e24)
  - homepage: https://diffusion-tokenflow.github.io (fetched 2026-08-29T11:11:21.443910+00:00, sha cdd661119aa9)
- Data as of 2026-08-30T08:39:29.467469+00:00.