223 citations · 727 across the 52 of their papers we have counts for
5 papers · 2 filters
One-Step Diffusion Distillation via Deep Equilibrium Models
Zhengyang Geng, Ashwini Pokle, J. Zico Kolter
Diffusion models excel at producing high-quality samples but naively require hundreds of iterations, prompting multiple attempts to distill the generation process into a faster net…
DiffiT: Diffusion Vision Transformers for Image Generation
Ali Hatamizadeh, Jiaming Song, Guilin Liu +2
Diffusion models with their powerful expressivity and high sample quality have achieved State-Of-The-Art (SOTA) performance in the generative domain. The pioneering Vision Transfor…
Fast Training of Diffusion Models with Masked Transformers
Hongkai Zheng, Weili Nie, Arash Vahdat +1
We propose an efficient approach to train large diffusion models with masked transformers. While masked transformers have been extensively explored for representation learning, the…
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
Jiarui Xu, Sifei Liu, Arash Vahdat +3
We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panopti…
ISB: Image-to-Image Schrödinger Bridge
Guan-Horng Liu, Arash Vahdat, De-An Huang +3
We propose Image-to-Image Schrödinger Bridge (ISB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distribu…