LongCat-Video README

The project's README from github.com/meituan-longcat/LongCat-Video. As one markdown file: README.md.

LongCat-Video

LongCat-Video
LongCat-Video
LongCat-Video-Avatar 1.5
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Model Introduction

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

For more detail, please refer to the comprehensive LongCat-Video Technical Report.

🎥 Teaser Video

🔥 Latest News!!

Quick Start

Installation

Clone the repo:

git clone --single-branch --branch main https://github.com/meituan-longcat/LongCat-Video
cd LongCat-Video

Install dependencies:

# create conda environment
conda create -n longcat-video python=3.10
conda activate longcat-video

# install torch (configure according to your CUDA version)
pip install torch==2.6.0+cu124 torchvision==0.21.0+cu124 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124

# install flash-attn-2
pip install ninja 
pip install psutil 
pip install packaging 
pip install flash_attn==2.7.4.post1

# install other requirements
pip install -r requirements.txt

# install longcat-video-avatar requirements
conda install -c conda-forge librosa
conda install -c conda-forge ffmpeg
pip install -r requirements_avatar.txt

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.

Model Download

Models Description Download Link
LongCat-Video foundational video generation 🤗 Huggingface
LongCat-Video-Avatar single- and multi-character audio-driven video generation (wav2vec2) 🤗 Huggingface
LongCat-Video-Avatar-1.5 upgraded avatar model with Whisper-large-v3 audio encoder, distillation-based fast inference 🤗 Huggingface

Download models using huggingface-cli:

pip install "huggingface_hub[cli]"
huggingface-cli download meituan-longcat/LongCat-Video --local-dir ./weights/LongCat-Video
huggingface-cli download meituan-longcat/LongCat-Video-Avatar --local-dir ./weights/LongCat-Video-Avatar
huggingface-cli download meituan-longcat/LongCat-Video-Avatar-1.5 --local-dir ./weights/LongCat-Video-Avatar-1.5

Run Text-to-Video

# Single-GPU inference
torchrun run_demo_text_to_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile

# Multi-GPU inference
torchrun --nproc_per_node=2 run_demo_text_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile

Run Image-to-Video

# Single-GPU inference
torchrun run_demo_image_to_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile

# Multi-GPU inference
torchrun --nproc_per_node=2 run_demo_image_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile

Run Video-Continuation

# Single-GPU inference
torchrun run_demo_video_continuation.py --checkpoint_dir=./weights/LongCat-Video --enable_compile

# Multi-GPU inference
torchrun --nproc_per_node=2 run_demo_video_continuation.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile

Run Long-Video Generation

# Single-GPU inference
torchrun run_demo_long_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile

# Multi-GPU inference
torchrun --nproc_per_node=2 run_demo_long_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile

Run Interactive Video Generation

# Single-GPU inference
torchrun run_demo_interactive_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile

# Multi-GPU inference
torchrun --nproc_per_node=2 run_demo_interactive_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile

Run LongCat-Video-Avatar

💡 User tips for 1.5
  • 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.

LongCat-Video-Avatar-1.5

# Audio-Text-to-Video
torchrun --nproc_per_node=2 run_demo_avatar_single_audio_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video-Avatar-1.5 --stage_1=at2v --input_json=assets/avatar/single_example_1.json --use_distill --model_type avatar-v1.5 --use_int8

# Audio-Image-to-Video
torchrun --nproc_per_node=2 run_demo_avatar_single_audio_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video-Avatar-1.5 --stage_1=ai2v --input_json=assets/avatar/single_example_1.json --use_distill --model_type avatar-v1.5 --use_int8

# Audio-Text-to-Video and Video-Continuation
torchrun --nproc_per_node=2 run_demo_avatar_single_audio_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video-Avatar-1.5 --stage_1=at2v --input_json=assets/avatar/single_example_1.json --num_segments=5 --ref_img_index=10 --mask_frame_range=3 --use_distill --model_type avatar-v1.5 --use_int8

# Audio-Image-to-Video and Video-Continuation
torchrun --nproc_per_node=2 run_demo_avatar_single_audio_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video-Avatar-1.5 --stage_1=ai2v --input_json=assets/avatar/single_example_1.json --num_segments=5 --ref_img_index=10 --mask_frame_range=3 --use_distill --model_type avatar-v1.5 --use_int8

# Audio-Image-to-Video
torchrun --nproc_per_node=2 run_demo_avatar_multi_audio_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video-Avatar-1.5 --input_json=assets/avatar/multi_example_1.json --use_distill --model_type avatar-v1.5 --use_int8

# Audio-Image-to-Video and Video-Continuation
torchrun --nproc_per_node=2 run_demo_avatar_multi_audio_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video-Avatar-1.5 --input_json=assets/avatar/multi_example_1.json --num_segments=5 --ref_img_index=10 --mask_frame_range=3 --use_distill --model_type avatar-v1.5 --use_int8

Run Streamlit

# Single-GPU inference
streamlit 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.

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.

See the LICENSE file for the full license text.

Usage Considerations

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 [email protected] or scan the QR code to join our WeChat Group if you have any questions.

LongCat-Video (meituan-longcat/LongCat-Video)

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LongCat-Video
Что это такое?LongCat-Video — базовая модель генерации видео с 13,6 млрд параметров, которая объединяет в одной архитектуре Text-to-Video, Image-to-Video и Video-Continuation. Она рассчитана на эффективную генерацию длинных видео и рассматривается как первый шаг к «моделям мира».
Это готовое приложение или модель, которую я запускаю сам?Это модель, которую вы скачиваете и запускаете самостоятельно, а не облачный сервис: вы клонируете репозиторий, устанавливаете зависимости, загружаете веса с Hugging Face и запускаете Python-скрипты инференса или входящий в комплект интерфейс Streamlit локально на своём оборудовании.
Как это запустить?Склонируйте репозиторий, установите зависимости, затем скачайте веса модели с Hugging Face с помощью huggingface-cli, прежде чем запускать скрипты инференса для Text-to-Video, Image-to-Video, Video-Continuation или Avatar (также предусмотрено приложение на Streamlit).
Какое оборудование нужно для запуска?Нужен мощный GPU: FlashAttention-2 включён по умолчанию (можно заменить на FlashAttention-3 или xformers), а квантизация INT8 предлагается специально для снижения использования VRAM в модели аватара.
Какие ограничения стоит знать перед использованием?Авторы заявляют, что модель не была всесторонне протестирована для каждого сценария последующего использования, поэтому перед развёртыванием в чувствительных или рискованных сценариях вам нужно самостоятельно оценить точность, безопасность и справедливость результатов.
Что на самом деле покрывает лицензия MIT здесь?И веса модели, и вклад в репозиторий распространяются под лицензией MIT, однако эта лицензия не предоставляет никаких прав на использование товарных знаков или патентов Meituan.
Что такое LongCat-Video-Avatar?Это расширение базовой модели для анимации персонажей по аудио: оно превращает аудио вместе с текстом или изображением во входных данных в выразительные говорящие видео с поддержкой одно- и многопоточного аудио.
Что изменилось в обновлении Avatar-1.5?Аудиоэнкодер Wav2Vec2 заменён на Whisper-Large для более точной синхронизации губ, добавлена дистилляция шагов для быстрого инференса за 8 шагов, а модель лучше обобщается на стилизованные направления, такие как аниме и животные.
Какое разрешение и скорость можно ожидать при генерации видео?LongCat-Video генерирует видео 720p с частотой 30 кадров в секунду за считаные минуты, используя стратегию «от грубого к точному» по времени и пространству с Block Sparse Attention, и поддерживает вывод в 480P и 720P через флаг разрешения.
Почему проект называет себя шагом к «моделям мира»?Потому что модель изначально предобучена на задачах video-continuation, что позволяет ей создавать видео длительностью в минуты без цветового дрейфа и потери качества — эту возможность авторы рассматривают как первый шаг к «моделям мира».
What are similar projects?LTX-Video, LTX-2, Evoke, SolarWM, SparkDiffusion, CogVideo, DreamX-Creator, ComfyUI-MiniMaxH3-TimelineDirector
Which topics is it in?talking-head, digital-human, lipsync, ai-video-generation, grpo, streamlit, world-models, foundation-models, image-to-video, text-to-video, whisper, diffusion-models
Creator
meituan-longcat
Size
1.3 GB
Stars
9.2K
Updated
CommitMay 27
Age
11mo
Commits · 90 Days