# showlab/Tune-A-Video

[ICCV 2023] Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

Repository: https://github.com/showlab/Tune-A-Video
Canonical: https://ross.abutalabs.com/products/tune-a-video
Homepage: https://tuneavideo.github.io
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2023-10-25T02:00:06+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 96
- inputs: {"age_days": 1347, "days_push": 1044, "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 4364, forks 388 (observed 2026-08-28T04:08:46.609735+00:00)

## What it is
Tune-A-Video is the official PyTorch implementation of an ICCV 2023 paper that fine-tunes pre-trained text-to-image diffusion models (like Stable Diffusion) for text-to-video generation using only a single text-video pair. It introduces a spatio-temporal attention mechanism and one-shot tuning strategy, with DDIM inversion for improved consistency.

## Use cases
- generate short videos from a text prompt and one example video
- fine-tune stable diffusion for text-to-video generation
- create stylized video variations from a single reference clip
- edit videos with text prompts using diffusion models
- run one-shot video tuning on a GPU
- try text-to-video generation in a colab notebook

## When to choose
- you want research-grade one-shot text-to-video generation built on Stable Diffusion
- you have a single reference video and want to generate new videos with different subjects or styles
- you need the reference implementation of the Tune-A-Video paper for academic work

## When to avoid
- you need production-quality long or high-resolution video generation
- you lack a capable GPU, since diffusion fine-tuning is compute-intensive
- you want a maintained product with active updates rather than a research codebase

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, video-processing, image-processing
- domain: artificial-intelligence, deep-learning, image-processing
- platform: python
- tags: text-to-video, diffusion-models, stable-diffusion, one-shot-tuning, iccv-2023, research, video, gpu, linux

## Member repositories
- showlab/Tune-A-Video (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.609735+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 2026-08-29T18:21:25.873089+00:00, confidence not recorded.
  - readme: https://github.com/showlab/Tune-A-Video (fetched 2026-08-28T04:08:46.609735+00:00, sha a6a5522efbad)
  - homepage: https://tuneavideo.github.io (fetched 2026-08-29T09:10:00.808678+00:00, sha 27ac0006e77b)
- Data as of 2026-08-30T08:39:29.467469+00:00.