Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache
arXiv:2608.28670v1 Announce Type: new Abstract: Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step requires large matrix operations. Existing cache-based acceleration reduces redundant computation yet increases the VRAM footprint by storing intermediate states, which can directly constrain inference batch size. In this work, we propose a training-free acceleration method that performs stepwise forecasting for DiT sampling using a Barycentric Extrapolator. By leveraging barycentric extrapolation, our predictor is numerically stable and alleviates oscillatory artifacts analogous to the Runge phenomenon during forward forecasting. Across extensive experiments on both image and video generation, our approach provides a favorable trade-off between memory usage and perceptual quality, while delivering up to 3.30x end-to-end sampling speedup compared with baseline DiT inference.
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[Submitted on 24 Aug 2026]
Title:Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache
View a PDF of the paper titled Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache, by Chengjie Lu and 5 other authors
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Abstract:Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step requires large matrix operations. Existing cache-based acceleration reduces redundant computation yet increases the VRAM footprint by storing intermediate states, which can directly constrain inference batch size. In this work, we propose a training-free acceleration method that performs stepwise forecasting for DiT sampling using a Barycentric Extrapolator. By leveraging barycentric extrapolation, our predictor is numerically stable and alleviates oscillatory artifacts analogous to the Runge phenomenon during forward forecasting. Across extensive experiments on both image and video generation, our approach provides a favorable trade-off between memory usage and perceptual quality, while delivering up to 3.30x end-to-end sampling speedup compared with baseline DiT inference.
Comments: Accepted by ICITES 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.28670 [cs.CV]
(or arXiv:2608.28670v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.28670
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chengjie Lu [view email] [v1] Mon, 24 Aug 2026 16:11:56 UTC (15,776 KB)
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