activity
20182026
most citedImagen 3

4 citations · 4 across the 7 of their papers we have counts for

collaborators

8 papers

cs.LG2026

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…

cs.LG2026

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.…

cs.LG2026

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…

cs.LG2026

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…

cs.CV2024

Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion

Emiel Hoogeboom, Thomas Mensink, Jonathan Heek +3

Latent diffusion models have become the popular choice for scaling up diffusion models for high resolution image synthesis. Compared to pixel-space models that are trained end-to-e…

cs.CV2024★ 4 cited

Imagen 3

Imagen-Team-Google, :, Jason Baldridge +257

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred…

cs.LG2024

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…

cs.CL2018

The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval

Spencer Cappallo, Stacey Svetlichnaya, Pierre Garrigues +2

Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages. We propose to treat these ide…