We introduce LongCat-Video, a foundational video generation model with 13.6B parameters, delivering strong performance across Text-to-Video, Image-to-Video, and Video-Continuation generation tasks. It particularly excels in efficient and high-quality long video generation, representing our first step toward world models.
Key Features
🌟 Unified architecture for multiple tasks: LongCat-Video unifies Text-to-Video, Image-to-Video, and Video-Continuation tasks within a single video generation framework. It natively supports all these tasks with a single model and consistently delivers strong performance across each individual task.
🌟 Long video generation: LongCat-Video is natively pretrained on Video-Continuation tasks, enabling it to produce minutes-long videos without color drifting or quality degradation.
🌟 Efficient inference: LongCat-Video generates $720p$, $30fps$ videos within minutes by employing a coarse-to-fine generation strategy along both the temporal and spatial axes. Block Sparse Attention further enhances efficiency, particularly at high resolutions
🌟 Strong performance with multi-reward RLHF: Powered by multi-reward Group Relative Policy Optimization (GRPO), comprehensive evaluations on both internal and public benchmarks demonstrate that LongCat-Video achieves performance comparable to leading open-source video generation models as well as the latest commercial solutions.
May 21, 2026: 🚀 We release LongCat-Video-Avatar-1.5, an upgraded open-source framework for audio-driven human video generation. v1.5 replaces Wav2Vec2 with Whisper-Large for more accurate lip synchronization, achieves production-ready physical rationality and temporal stability with robust long-video generation, generalizes to stylized domains (anime, animals, complex real-world conditions), supports both single-stream and multi-stream audio inputs, and accelerates inference to 8 steps via step distillation. [ code | 🤗 weights | project page ]
Dec 16, 2025: 🚀 We are excited to announce the release of LongCat-Video-Avatar, a unified model that delivers expressive and highly dynamic audio-driven character animation, supporting native tasks including Audio-Text-to-Video, Audio-Text-Image-to-Video, and Video Continuation with seamless compatibility for both single-stream and multi-stream audio inputs. The release includes our Technical Report, inference code, 🤗 model weights, and project page.
Oct 25, 2025: 🚀 We've released LongCat-Video, a foundational video generation model. Tech report and models are available at LongCat-Video Technical Report and 🤗 Huggingface !
Quick Start
Installation
Clone the repo:
shell
1git clone --single-branch --branch main https://github.com/meituan-longcat/LongCat-Video
2cd LongCat-Video
FlashAttention-2 is enabled in the model config by default; you can also change the model config ("./weights/LongCat-Video/dit/config.json") to use FlashAttention-3 or xformers once installed.
Lip synchronization accuracy: Audio CFG works optimally between 3–5. Increase the audio CFG value for better synchronization.
Prompt Enhancement: Longer, more descriptive prompts yield better consistency and naturalness than short ones. We recommend including rich details such as character appearance, actions, and scene context (e.g., "A young woman with long black hair is speaking and smiling, wearing a white blouse, sitting in a bright café") for best results.
Mitigate repeated actions: Setting the reference image index（--ref_img_index, default to 10） between 0 and 24 ensures better consistency; setting it to 30 helps reduce repeated actions. Additionally, increasing the mask frame range (--mask_frame_range, default to 3) can further help mitigate repeated actions, but excessively large values may introduce artifacts.
Super resolution: Our model is compatible with both 480P and 720P, which can be controlled via --resolution.
Dual-Audio Modes: Merge mode (set audio_type to para) requires two audio clips of equal length, and the resulting audio is obtained by summing the two clips; Concatenation mode (set audio_type to add) does not require equal-length inputs, and the resulting audio is formed by sequentially concatenating the two clips with silence padding for any gaps, where by default person1 speaks first and person2 speaks afterward.
Model versions:--model_type avatar-v1.0 uses wav2vec2 audio encoder (default); --model_type avatar-v1.5 uses Whisper-large-v3 audio encoder for better lip sync quality.
Distillation mode: Add --use_distill to enable distillation sampling (fewer steps, faster inference). This is required when using --model_type avatar-v1.5.
INT8 quantization: Add --use_int8 to load the INT8 quantized DiT model for reduced VRAM usage. Only supported with --model_type avatar-v1.5.
💡 User tips for 1.0
Lip synchronization accuracy: Audio CFG works optimally between 3–5. Increase the audio CFG value for better synchronization.
Prompt Enhancement: Include clear verbal-action cues (e.g., talking, speaking) in the prompt to achieve more natural lip movements.
Mitigate repeated actions: Setting the reference image index（--ref_img_index, default to 10） between 0 and 24 ensures better consistency, while selecting other ranges (e.g., -10 or 30) helps reduce repeated actions. Additionally, increasing the mask frame range (--mask_frame_range, default to 3) can further help mitigate repeated actions, but excessively large values may introduce artifacts.
Super resolution: Our model is compatible with both 480P and 720P, which can be controlled via --resolution.
Dual-Audio Modes: Merge mode (set audio_type to para) requires two audio clips of equal length, and the resulting audio is obtained by summing the two clips; Concatenation mode (set audio_type to add) does not require equal-length inputs, and the resulting audio is formed by sequentially concatenating the two clips with silence padding for any gaps, where by default person1 speaks first and person2 speaks afterward.
1# Single-GPU inference2streamlit run ./run_streamlit.py --server.fileWatcherType none --server.headless=false
Evaluation Results
Text-to-Video
The Text-to-Video MOS evaluation results on our internal benchmark.
MOS score
Veo3
PixVerse-V5
Wan 2.2-T2V-A14B
LongCat-Video
Accessibility
Proprietary
Proprietary
Open Source
Open Source
Architecture
-
-
MoE
Dense
# Total Params
-
-
28B
13.6B
# Activated Params
-
-
14B
13.6B
Text-Alignment↑
3.99
3.81
3.70
3.76
Visual Quality↑
3.23
3.13
3.26
3.25
Motion Quality↑
3.86
3.81
3.78
3.74
Overall Quality↑
3.48
3.36
3.35
3.38
Image-to-Video
The Image-to-Video MOS evaluation results on our internal benchmark.
MOS score
Seedance 1.0
Hailuo-02
Wan 2.2-I2V-A14B
LongCat-Video
Accessibility
Proprietary
Proprietary
Open Source
Open Source
Architecture
-
-
MoE
Dense
# Total Params
-
-
28B
13.6B
# Activated Params
-
-
14B
13.6B
Image-Alignment↑
4.12
4.18
4.18
4.04
Text-Alignment↑
3.70
3.85
3.33
3.49
Visual Quality↑
3.22
3.18
3.23
3.27
Motion Quality↑
3.77
3.80
3.79
3.59
Overall Quality↑
3.35
3.27
3.26
3.17
Community Works
Community works are welcome! Please PR or inform us in Issue to add your work.
CacheDiT offers Fully Cache Acceleration support for LongCat-Video with DBCache and TaylorSeer, achieved nearly 1.7x speedup without obvious loss of precision. Visit their example for more details.
License Agreement
The model weights are released under the MIT License.
Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents.
This model has not been specifically designed or comprehensively evaluated for every possible downstream application.
Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios.
It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements.
Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.
Citation
We kindly encourage citation of our work if you find it useful.
@misc{meituanlongcatteam2025longcatvideotechnicalreport,
title={LongCat-Video Technical Report},
author={Meituan LongCat Team and Xunliang Cai and Qilong Huang and Zhuoliang Kang and Hongyu Li and Shijun Liang and Liya Ma and Siyu Ren and Xiaoming Wei and Rixu Xie and Tong Zhang},
year={2025},
eprint={2510.22200},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.22200},
}
@misc{meituanlongcatteam2026longcatvideoavatar15technicalreport,
title={LongCat-Video-Avatar 1.5 Technical Report},
author={Meituan LongCat Team and Xunliang Cai and Meng Cheng and Feng Gao and Zhe Kong and Jiamu Li and Le Li and Weiheng Li and Hongyu Liu and Shuai Tan and Xiaoming Wei and Tianyu Yang and Yong Zhang},
year={2026},
eprint={2605.26486},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.26486},
}
@misc{meituanlongcatteam2025longcatvideoavatartechnicalreport,
title={LongCat-Video-Avatar Technical Report},
author={Meituan LongCat Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={},
}
Acknowledgements
We would like to thank the contributors to the Wan, UMT5-XXL, Diffusers and HuggingFace repositories, for their open research.
Contact
Please contact us at longcat-team@meituan.com or scan the QR code to join our WeChat Group if you have any questions.
