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

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.

Image-to-Video

The Image-to-Video MOS evaluation results on our internal benchmark.

Community Works

Community works are welcome! Please PR or inform us in I