In this paper, we introduce Matten, a cutting-edge latent diffusion model
with Mamba-Attention architecture for video generation. With minimal
computational cost, Matten employs spatial-temporal attention for local video
content modeling and bidirectional Mamba for global video content modeling. Our
comprehensive experimental evaluation demonstrates that Matten has competitive
performance with the current Transformer-based and GAN-based models in
benchmark performance, achieving superior FVD scores and efficiency.
Additionally, we observe a direct positive correlation between the complexity
of our designed model and the improvement in video quality, indicating the
excellent scalability of Matten.

本文介绍了一种名为 Matten 的先进的潜在扩散模型，采用 Mamba-Attention 架构用于视频生成。Matten 以最小的计算成本，通过空间 - 时间注意力对本地视频内容建模，并通过双向 Mamba 对全局视频内容进行建模。我们的综合实验评估表明，Matten 在基准性能上与当前基于 Transformer 和 GAN 的模型竞争力强，取得了优秀的分数和高效性。此外，我们观察到我们设计模型的复杂性与视频质量提高之间存在直接正相关关系，表明 Matten 具有出色的可扩展性。