1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Rethinking Token-wise Feature Caching: Accelerating Diffusion Transformers with Dual Feature Caching
Chang Zou, Shikang Zheng, Evelyn Zhang +6
Diffusion Transformers (DiT) have become the dominant methods in image and video generation yet still suffer substantial computational costs. As an effective approach for DiT accel…