LongCat-Video - Meituan's open-source AI video generation model

What is LongCat-Video?

LongCat-Video is an open-source AI video generation model with 13.6 billion parameters developed by Meituan's LongCat team. It excels in tasks such as text-to-video, image-to-video, and video continuation, and is particularly adept at efficiently generating high-quality long videos. Through multi-reward reinforcement learning optimization (GRPO), the model demonstrates performance comparable to leading open-source video generation models and state-of-the-art commercial solutions in internal and public benchmark tests.

Main functions of LongCat-Video

• Long video generationIt was pre-trained on video continuation tasks and can generate videos several minutes long without color shift or quality degradation.
• Multi-task unified architectureIt unifies text-to-video, image-to-video, and video continuation tasks within a single video generation framework, requiring only one model to complete all tasks.
• Efficient ReasoningBy employing a coarse-to-fine generation strategy and Block Sparse Attention technology, it is possible to generate 720p, 30fps videos within minutes.
• Multi-reward reinforcement learning optimizationLeveraging the multi-reward Group Relative Policy Optimization (GRPO), it demonstrates performance comparable to leading open-source video generation models and the latest commercial solutions in internal and public benchmarks.

LongCat-Video's technical principles

• Unified architectureIt adopts a single video generation framework that integrates multiple tasks such as text-to-video, image-to-video, and video continuation. By sharing model architecture and parameters, it achieves efficient processing of different tasks.
• Long video generation technologyThe model is pre-trained on video continuation tasks, using specific training strategies and algorithms to enable it to generate long videos lasting several minutes while maintaining the coherence and quality stability of the video content.
• Efficient reasoning strategiesEmploying a coarse-to-fine generation strategy, the model first generates a rough framework of the video and then gradually refines the details. Combined with Block Sparse Attention technology, this improves the inference efficiency of the model when generating high-resolution videos and shortens the generation time.
• Multi-reward reinforcement learning optimizationBy leveraging the Group Relative Policy Optimization (GRPO) method, the model is optimized and trained to achieve better performance across multiple dimensions, including text alignment, visual quality, and motion quality, thereby improving the overall quality of the generated video.

LongCat-Video's project address

• Project official website: https://meituan-longcat.github.io/LongCat-Video/
• Github repository: https://github.com/meituan-longcat/LongCat-Video
• HuggingFace model libraryhttps://huggingface.co/meituan-longcat/LongCat-Video

Application scenarios of LongCat-Video

• Content creationIt helps creators quickly generate video materials, such as advertising videos, short videos, and animations, thereby improving creative efficiency.
• Video continuationGenerate follow-up content for existing video clips, used in scenarios such as story expansion and video editing.
• Education and TrainingGenerate teaching videos, demonstration videos, etc., to assist in the teaching and training process and enhance the learning experience.
• Entertainment and GamesGenerate dynamic scenes or character animations during game development to enhance the game's visual effects and immersion.
• Intelligent customer service and virtual assistantGenerate video responses to provide users with a more intuitive interactive experience.
• Creative Design: Assist designers in creating video concept designs and quickly presenting creative ideas.