1 citations · 1 across the 4 of their papers we have counts for
4 papers
DiSC: Resolution-Scalable Acceleration of Diffusion Models by Exploiting Sparsity and Cached Token Reuse with Hash-based Distribution
Jieon Yoon, Hangyeol Lee, Jaehoon Heo +1
Transformer-based diffusion models offer superior scalability and performance but suffer from high computational overhead due to the iterative nature and quadratic complexity of se…
ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration
Hangyeol Lee, Joo-Young Kim
Diffusion Transformer (DiT) has emerged as a powerful model architecture for generating high-quality images and videos. In the case of video DiT, 3D Spatio-Temporal Attention incre…
Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness
Hangyeol Lee, Hyojeong Lee, Joo-Young Kim
Diffusion Transformers (DiTs) achieve superior image generation quality but suffer from quadratic computational complexity relative to token count. While various token reduction (T…
EXION: Exploiting Inter- and Intra-Iteration Output Sparsity for Diffusion Models
Jaehoon Heo, Adiwena Putra, Jieon Yoon +4
Over the past few years, diffusion models have emerged as novel AI solutions, generating diverse multi-modal outputs from text prompts. Despite their capabilities, they face challe…