Home Projects AnyV2V
AnyV2V
Jupyter Notebook

AnyV2V

Code and data for "AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks" [TMLR 2024]

by TIGER-AI-Lab · GitHub
Stars
Forks
Trending
License
Created
Last commit
deep-learninggenerative-aiimage-editingMITJupyter Notebook
View on GitHub
In plain words

Edit videos using just a single image without needing to train any models.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
AnyV2V — GitHub preview card
📈 Star history
656654
2026-07-202026-08-31
📈 Track AnyV2V

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Code and data for "AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks" [TMLR 2024]

AnyV2V has 656 stars on GitHub. It has been forked 49 times. AnyV2V is written mainly in Jupyter Notebook. It has been in active development since 2024. AnyV2V is available under the MIT license. Its main topics are deep-learning, generative-ai, image-editing, image-to-video-generation.

Frequently asked questions

What is AnyV2V?

Code and data for "AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks" [TMLR 2024]

Is AnyV2V open source?

AnyV2V is an open-source project. It is released under the MIT license.

Is AnyV2V free?

Yes. AnyV2V is free and open source — you can use, modify and self-host it.

What license does AnyV2V use?

AnyV2V is available under the MIT license.

What language is AnyV2V written in?

AnyV2V is written mainly in Jupyter Notebook.

🏅 Maintainer of this project?
olud.ai badge — AnyV2V

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=tiger-ai-lab-anyv2v)](https://olud.ai/project/tiger-ai-lab-anyv2v.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.