4 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps
Yeongmin Kim, Arnaud Doucet, Andrew Campbell +3
We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive language (AR) model. We theoretica…
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…