Rethinking Cross-Layer Information Routing in Diffusion Transformers
The paper discusses improvements in Diffusion Transformers (DiTs) through a new method called Diffusion-Adaptive Routing (DAR). This method addresses issues with traditional residual addition in DiTs, enhancing information flow across layers. The authors demonstrate that DAR significantly improves performance while reducing training time.
- ▪Diffusion Transformers have become essential in visual generation, but their residual stream design has not been significantly altered.
- ▪The paper identifies three issues with traditional residual addition: monotonic forward magnitude inflation, sharp backward gradient decay, and block-wise redundancy.
- ▪The proposed DAR method allows for learnable, timestep-adaptive aggregation of sublayer outputs, improving training efficiency and model performance.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20708 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20708 (cs) [Submitted on 20 May 2026] Title:Rethinking Cross-Layer Information Routing in Diffusion Transformers Authors:Chao Xu, Maohua Li, Qirui Li, Yixuan Xu, Yanke Zhou, Yunhe Li, Cuifeng Shen, Hanlin Tang, Kan Liu, Tao Lan, Lin Qu, Shao-Qun Zhang View a PDF of the paper titled Rethinking Cross-Layer Information Routing in Diffusion Transformers, by Chao Xu and 11 other authors View PDF HTML (experimental) Abstract:Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, objectives, and latent autoencoders -- has been extensively revisited.
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