Author

Linfeng Zhang

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Preprint Aug 2026

AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation

Diffusion Transformers (DiTs) achieve high-quality generation but are costly due to iterative sampling. Dynamic-resolution sampling reduces early-stage cost by denoising at low resolution; however, uniformly upsampling all latent tokens at resolution transitions incurs redundant computation and may degrade fine-detail...

Haoran Qin, Zhen Yan, Shikang Zheng et al. · 0 citations

#machine learning Preprint Sep 2026

Accelerating Diffusion Transformers with Gaussian Process Rectified Feature Cache

Diffusion Transformers have become the dominant paradigm in generative AI, but their high computational costs severely hinder real-time applications. Prediction-based feature caching is widely used to accelerate diffusion transformers; however, as the number of steps increases, the deviation between its predictions and...

Zhi-Rong Shen, Rui-Xin Huang, Chang Zou et al. · 0 citations

Preprint Aug 2026

LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching

LinCa decomposes cached features into sub-components with distinct continuity properties via a lightweight invertible network and applies differentiated prediction orders matched to each component, forming a unified Decompose-Predict-Reconstruct pipeline.

Jin-Shan Liu, Hao-Ran Qin, Xiao-Bing Tu et al. · 0 citations