LongCat-Video Technical Report
Video generation is a critical pathway toward world models, with efficient
long video inference as a key capability. Toward this end, we introduce
LongCat-Video, a foundational video generation model with 13.6B parameters,
delivering strong performance across multiple video generation tasks. It
particularly excels in efficient and high-quality long video generation,
representing our first step toward world models. Key features include: Unified
architecture for multiple tasks: Built on the Diffusion Transformer (DiT)
framework, LongCat-Video supports Text-to-Video, Image-to-Video, and
Video-Continuation tasks with a single model; Long video generation:
Pretraining on Video-Continuation tasks enables LongCat-Video to maintain high
quality and temporal coherence in the generation of minutes-long videos;
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: Multi-reward
RLHF training enables LongCat-Video to achieve performance on par with the
latest closed-source and leading open-source models. Code and model weights are
publicly available to accelerate progress in the field.
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Meituan LongCat Team
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Xunliang Cai
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Qilong Huang
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Zhuoliang Kang
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Hongyu Li
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Shijun Liang
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Liya Ma
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Siyu Ren
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Xiaoming Wei
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Rixu Xie
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Tong Zhang
LongCat-Video Technical Report
Video generation is a critical pathway toward world models, with efficient
long video inference as a key capability. Toward this end, we introduce
LongCat-Video, a foundational video generation model with 13.6B parameters,
delivering strong performance across multiple video generation tasks. It
particularly excels in efficient and high-quality long video generation,
representing our first step toward world models. Key features include: Unified
architecture for multiple tasks: Built on the Diffusion Transformer (DiT)
framework, LongCat-Video supports Text-to-Video, Image-to-Video, and
Video-Continuation tasks with a single model; Long video generation:
Pretraining on Video-Continuation tasks enables LongCat-Video to maintain high
quality and temporal coherence in the generation of minutes-long videos;
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: Multi-reward
RLHF training enables LongCat-Video to achieve performance on par with the
latest closed-source and leading open-source models. Code and model weights are
publicly available to accelerate progress in the field.
LongCat-Video, a 13.6B parameter video generation model based on the Diffusion Transformer framework, excels in efficient and high-quality long video generation across multiple tasks using unified architecture, coarse-to-fine generation, and block sparse attention.
Diffusion Transformer Text-to-Video Image-to-Video Video-Continuation temporal coherence coarse-to-fine generation block sparse attention multi-reward RLHF