1 citations · 1 across the 10 of their papers we have counts for
15 papers
Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity
Mahmud Hasan, Niloofar Ramezani, Hrishikesh Chakraborty
Longitudinal health data often exhibit within-subject correlation, subject-specific heterogeneity, and complex nonlinear predictor--response relationships that may not be adequatel…
Accelerating Diffusion Transformers with Gaussian Process Rectified Feature Cache
Zhirong Shen, Rui Huang, Chang Zou +10
Diffusion Transformers have become the dominant paradigm in generative AI, but their high computational costs severely hinder real-time applications. Prediction-based feature cachi…
AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation
Haoran Qin, Zhengan Yan, Shikang Zheng +9
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 res…
LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching
Jinshan Liu, Haoran Qin, Xiaobing Tu +9
Diffusion models have achieved remarkable success in image and video generation, yet the high computational cost of iterative sampling remains a critical bottleneck for practical d…
SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking
Zhengan Yan, Shikang Zheng, Haoran Qin +9
Diffusion-based image editing offers strong semantic controllability, but remains computationally expensive due to iterative high-resolution denoising over all spatial tokens. Dyna…
Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models
Zhirong Shen, Rui Huang, Jiacheng Liu +6
To address the high sampling cost of Diffusion Transformers (DiTs), feature caching offers a training-free acceleration method. However, existing methods rely on hand-crafted forec…
LESA: Learnable Stage-Aware Predictors for Diffusion Model Acceleration
Peiliang Cai, Jiacheng Liu, Haowen Xu +3
Diffusion models have achieved remarkable success in image and video generation tasks. However, the high computational demands of Diffusion Transformers (DiTs) pose a significant c…
From Sketch to Fresco: Efficient Diffusion Transformer with Progressive Resolution
Shikang Zheng, Guantao Chen, Lixuan He +4
Diffusion Transformers achieve impressive generative quality but remain computationally expensive due to iterative sampling. Recently, dynamic resolution sampling has emerged as a…
A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation
Jiacheng Liu, Xinyu Wang, Yuqi Lin +10
Diffusion Models have become a cornerstone of modern generative AI for their exceptional generation quality and controllability. However, their inherent \textit{multi-step iteratio…
FreqCa: Accelerating Diffusion Models via Frequency-Aware Caching
Jiacheng Liu, Peiliang Cai, Qinming Zhou +9
The application of diffusion transformers is suffering from their significant inference costs. Recently, feature caching has been proposed to solve this problem by reusing features…
Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers
Shikang Zheng, Guantao Chen, Qinming Zhou +6
Diffusion Transformers offer state-of-the-art fidelity in image and video synthesis, but their iterative sampling process remains a major bottleneck due to the high cost of transfo…
SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu +4
Diffusion models have revolutionized high-fidelity image and video synthesis, yet their computational demands remain prohibitive for real-time applications. These models face two f…
Forecast then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers
Shikang Zheng, Liang Feng, Xinyu Wang +8
Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To reduce their substantial computational costs, feature cachin…
HiCache: A Plug-in Scaled-Hermite Upgrade for Taylor-Style Cache-then-Forecast Diffusion Acceleration
Liang Feng, Shikang Zheng, Jiacheng Liu +8
Diffusion models have achieved remarkable success in content generation but often incur prohibitive computational costs due to iterative sampling. Recent feature caching methods ac…
From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu +2
Diffusion Transformers (DiT) have revolutionized high-fidelity image and video synthesis, yet their computational demands remain prohibitive for real-time applications. To solve th…