meituan-longcat/LongCat-Video

LongCat-Video

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

• 🌟 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.

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

🎥 Teaser Video

Quick Start

Installation

Clone the repo:

git clone 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

FlashAttention-2 is enabled in the model config by default; you can also change the model config to use FlashAttention-3 or xformers.

Model Download

| Models | Download Link | | --- | --- | | LongCat-Video | 🤗 Huggingface |

Download models using huggingface-cli:

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

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 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.

• 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…