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LongCat-Video

MIT-licensed video model from Meituan for text, image and long-video generation with continuation.

MIT-licensed model for long video clips

LongCat-Video covers text-to-video, image-to-video and video continuation.

Free forever · open source · MIT
✓ Needs IT✓ Browser-based✓ Real-time collaboration
6.8
ZEKAI
MIT long-video model
Free & open source · Software
Ease of setup3
Usefulness6
Maintenance9.5
Community8.5
★ 9,430
GITHUB
Nov 2026
UPDATED
MIT
LICENSE
🛠 Don't want to self-host? We can set it up →
⚡ Quick answer

LongCat-Video is an open video generation model from Meituan that handles text-to-video, image-to-video and video continuation in one model. It suits developers who want MIT-licensed weights for long clips.

Runs on
Cross-platform
Hosting
Self-host
Languages
Python
License
MIT

What it is

The repo includes inference code for text-to-video, image-to-video, video continuation, long-video generation and interactive generation, plus audio-driven avatar models and a Streamlit interface.

Who it's for

It suits developers with CUDA GPUs and the patience to build FlashAttention. It does not suit non-technical users, and the README states no VRAM figure, so size your hardware by trial.

Key features

Three tasks, one model

Text-to-video, image-to-video and continuation share a model.

Long-video generation

Built to extend clips well beyond a single short generation.

Interactive generation

The README lists an interactive video mode.

Avatar models

Audio-driven single and multi-audio avatar video is included.

Streamlit interface

A bundled Streamlit app avoids writing inference scripts.

Multi-GPU and int8

Multi-GPU runs and an int8 option for the avatar model.

How to start

Up and running in 3 steps

1
Clone the repository and create a Python environment with the CUDA 12.4 PyTorch build the README names.
2
Install FlashAttention-2 and the other requirements, then download the weights listed in the README.
3
Run the text-to-video or image-to-video script, or launch the Streamlit app, and add --nproc_per_node for multi-GPU.
Real talk

Our honest verdict

What's great

  • Repository code and the model weights are both MIT-licensed, per the README.
  • One model handles text, image and continuation, so you do not juggle separate checkpoints.
  • Includes a Streamlit interface in addition to command-line scripts.
  • Active: the README lists LongCat-Video-Avatar-1.5 released in May 2026.

Where it falls short

  • The README states no minimum VRAM, so you must find your own limits with the large weights.
  • Requires CUDA 12.4 PyTorch builds and FlashAttention-2, which can take effort to compile.
  • Aimed at developers; there is no installer or desktop app.
  • The repo has no tagged releases, so versions are tracked by commit and Hugging Face model pages.
Cost

Pricing & license

Free under the MIT licence. The only cost is the GPU hardware and storage you run it on; no paid hosted plan appears in the repo.

Paid work

Open remote AI-training roles

870 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.

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Questions

FAQ

Is LongCat-Video free for commercial use?

The README says the model weights are released under the MIT License, which allows commercial use. It adds that this grants no rights to Meituan trademarks or patents.

What can LongCat-Video generate?

Text-to-video, image-to-video, video continuation, long-video generation, interactive video and audio-driven avatar video, per the README.

How much GPU memory does it need?

The README gives no VRAM figure. It mentions multi-GPU runs and an int8 option that cuts memory for the avatar model.

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