S2DiT: Sandwich Diffusion Transformer for Mobile Streaming Video Generation

• URL: http://arxiv.org/abs/2601.12719v1
• Date: Mon, 19 Jan 2026 04:48:21 GMT
• Title: S2DiT: Sandwich Diffusion Transformer for Mobile Streaming Video Generation
• Authors: Lin Zhao, Yushu Wu, Aleksei Lebedev, Dishani Lahiri, Meng Dong, Arpit Sahni, Michael Vasilkovsky, Hao Chen, Ju Hu, Aliaksandr Siarohin, Sergey Tulyakov, Yanzhi Wang, Anil Kag, Yanyu Li,
• Abstract summary: S2DiT is a Streaming Sandwich Diffusion Transformer designed for efficient, high-fidelity, and streaming video generation on mobile hardware.<n>We propose a 2-in-1 distillation framework that transfers the capacity of large teacher models to the compact few-step sandwich model.<n>S2DiT achieves quality on par with state-of-the-art server video models, while streaming at over 10 FPS on an iPhone.
• Score: 77.08147098854762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion Transformers (DiTs) have recently improved video generation quality. However, their heavy computational cost makes real-time or on-device generation infeasible. In this work, we introduce S2DiT, a Streaming Sandwich Diffusion Transformer designed for efficient, high-fidelity, and streaming video generation on mobile hardware. S2DiT generates more tokens but maintains efficiency with novel efficient attentions: a mixture of LinConv Hybrid Attention (LCHA) and Stride Self-Attention (SSA). Based on this, we uncover the sandwich design via a budget-aware dynamic programming search, achieving superior quality and efficiency. We further propose a 2-in-1 distillation framework that transfers the capacity of large teacher models (e.g., Wan 2.2-14B) to the compact few-step sandwich model. Together, S2DiT achieves quality on par with state-of-the-art server video models, while streaming at over 10 FPS on an iPhone.

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