MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation

Weinan Jia, Yuning Lu, Mengqi Huang, Hualiang Wang, binyuanhuang, Nan Chen, Mu Liu, Jidong Jiang, Zhendong Mao

MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation: 41 upvotes on Hugging Face Daily Papers, #8 of 37 papers on 2025-10-22. Day-by-day upvote history.

Long video generation with Diffusion Transformers (DiTs) is bottlenecked by the quadratic scaling of full attention with sequence length. Since attention is highly redundant, outputs are dominated by a small subset of query-key pairs. Existing sparse methods rely on blockwise coarse estimation, whose accuracy-efficiency trade-offs are constrained by block size. This paper introduces Mixture-of-Groups Attention (MoGA), an efficient sparse attention that uses a lightweight, learnable token router to precisely match tokens without blockwise estimation. Through semantic-aware routing, MoGA enables effective long-range interactions. As a kernel-free method, MoGA integrates seamlessly with modern attention stacks, including FlashAttention and sequence parallelism. Building on MoGA, we develop an efficient long video generation model that end-to-end produces minute-level, multi-shot, 480p videos at 24 fps, with a context length of approximately 580k. Comprehensive experiments on various video generation tasks validate the effectiveness of our approach.

Paper page on Hugging Face · arXiv

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