Author

Tianchi Deng

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Preprint Aug 2026

Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache

This work proposes a training-free acceleration method that performs stepwise forecasting for DiT sampling using a Barycentric Extrapolator and 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.

Cheng-Jie Lu, Tianchi Deng, Zheng He et al. · 0 citations

Preprint Aug 2026

ChebBooster: A Training-Free Approach for Efficient Diffusion Transformer Inference via Chebyshev-Inspired Extrapolation

CebBooster is proposed, a training-free extrapolation framework based on Chebyshev polynomial theory that achieves stable and efficient acceleration for DiTs and outperforming existing training-free baselines under diverse generation tasks and resolutions.

Cheng-Jie Lu, Tianchi Deng, Zheng He et al. · 0 citations