4 citations · 7 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Accelerating Diffusion Transformers with Token-wise Feature Caching
Chang Zou, Xuyang Liu, Ting Liu +2
Diffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. To address this problem, feature caching met…