# test-time-training/ttt-video-dit

Official PyTorch implementation of One-Minute Video Generation with Test-Time Training

Repository: https://github.com/test-time-training/ttt-video-dit
Canonical: https://ross.abutalabs.com/products/ttt-video-dit
Homepage: https://test-time-training.github.io/video-dit
Language: Python
License: MIT
License Family: permissive
Last push: 2026-02-25T04:11:26+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 69, release rhythm 35, longevity 36
- inputs: {"age_days": 513, "days_push": 189, "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 2448, forks 9 (observed 2026-08-28T04:06:52.718788+00:00)

## What it is
Official PyTorch implementation of 'One-Minute Video Generation with Test-Time Training', which finetunes the CogVideoX 5B diffusion transformer with TTT layers for long-range video generation and style transfer. It includes training and inference code for generating 63-second videos from text storyboards.

## Use cases
- generate one-minute videos from text storyboards
- finetune a diffusion transformer for video style transfer
- extend video generation context beyond a few seconds
- experiment with test-time training layers in transformers
- reproduce research results comparing TTT layers to Mamba 2 and sliding-window attention

## When to choose
- you need to generate long, multi-scene videos with strong temporal consistency
- you want to experiment with TTT layers in diffusion transformers
- you have H100 GPUs and want to train or sample from the research model

## When to avoid
- you lack H100-class GPUs or CUDA 12.3+ for the TTT-MLP kernel
- you need production-ready, artifact-free video generation
- you only need short 3-second clips from the pretrained CogVideoX model

## Facets
- artifact type: library
- maturity: experimental
- function: deep-learning, machine-learning, video-processing, llm-training
- domain: deep-learning, machine-learning, artificial-intelligence
- platform: python
- tags: diffusion-transformer, text-to-video, test-time-training, cogvideox, research-code, pytorch, video, gpu, linux

## Member repositories
- test-time-training/ttt-video-dit (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:52.718788+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 2026-08-30T02:30:29.837135+00:00, confidence not recorded.
  - readme: https://github.com/test-time-training/ttt-video-dit (fetched 2026-08-28T04:06:52.718788+00:00, sha e75f7f8f3ddf)
  - homepage: https://test-time-training.github.io/video-dit (fetched 2026-08-29T10:11:55.110483+00:00, sha 648971d8fd00)
- Data as of 2026-08-30T08:39:29.467469+00:00.