67 citations · 70 across the 5 of their papers we have counts for
5 papers
One-step Diffusion Models with -Divergence Distribution Matching
Yilun Xu, Weili Nie, Arash Vahdat
Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications. To accelerate generation speed, r…
Heavy-Tailed Diffusion Models
Kushagra Pandey, Jaideep Pathak, Yilun Xu +4
Diffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unc…
Energy-Based Diffusion Language Models for Text Generation
Minkai Xu, Tomas Geffner, Karsten Kreis +5
Despite remarkable progress in autoregressive language models, alternative generative paradigms beyond left-to-right generation are still being actively explored. Discrete diffusio…
Truncated Consistency Models
Sangyun Lee, Yilun Xu, Tomas Geffner +4
Consistency models have recently been introduced to accelerate sampling from diffusion models by directly predicting the solution (i.e., data) of the probability flow ODE (PF ODE)…
A Survey on Generative Diffusion Model
Hanqun Cao, Cheng Tan, Zhangyang Gao +4
Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing A…