2.1k citations · 2.9k across the 8 of their papers we have counts for
16 papers · 1 filter
Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network
Grigory Bartosh, David Ruhe, Emiel Hoogeboom +3
Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluation of a heavy neural network.…
Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD
Emiel Hoogeboom, David Ruhe, Jonathan Heek +2
It is currently difficult to distill discrete diffusion models. In contrast, continuous diffusion literature has many distillation approaches methods that can reduce sampling steps…
Unified Latents (UL): How to train your latents
Jonathan Heek, Emiel Hoogeboom, Thomas Mensink +1
We present Unified Latents (UL), a framework for learning latent representations that are jointly regularized by a diffusion prior and decoded by a diffusion model. By linking the…
Multistep Distillation of Diffusion Models via Moment Matching
Tim Salimans, Thomas Mensink, Jonathan Heek +1
We present a new method for making diffusion models faster to sample. The method distills many-step diffusion models into few-step models by matching conditional expectations of th…
EM Distillation for One-step Diffusion Models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma +6
While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, bu…
Multistep Consistency Models
Jonathan Heek, Emiel Hoogeboom, Tim Salimans
Diffusion models are relatively easy to train but require many steps to generate samples. Consistency models are far more difficult to train, but generate samples in a single step.…