3 accepted papers
Diffusion transformers have achieved remarkable progress in high-quality image and video generation, but their computational overhead remains a significant challenge. Existing token reduction-based acceleration techniques, such as caching and merging, attempt to reduce this cost from both temporal a
Diffusion transformers have shown exceptional performance in visual generation but incur high computational costs. Token reduction techniques that compress models by sharing the denoising process among similar tokens have been introduced. However, existing approaches neglect the denoising priors of…
Token reduction is an efficient method for accelerating vision transformers. Techniques like token pruning and merging progressively decrease the number of active tokens to reduce the computation cost. However, when applied to dense prediction tasks, these techniques crudely cache low-level features…