309 citations · 550 across the 15 of their papers we have counts for
15 papers
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…
Model Integrity when Unlearning with T2I Diffusion Models
Andrea Schioppa, Emiel Hoogeboom, Jonathan Heek
The rapid advancement of text-to-image Diffusion Models has led to their widespread public accessibility. However these models, trained on large internet datasets, can sometimes ge…
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…
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…
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.…
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Mostafa Dehghani, Basil Mustafa, Josip Djolonga +12
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challeng…
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets
Jonas Ngnawe, Marianne Abemgnigni Njifon, Jonathan Heek +1
Deep networks have achieved impressive results on a range of well-curated benchmark datasets. Surprisingly, their performance remains sensitive to perturbations that have little ef…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…
Simple diffusion: End-to-end diffusion for high resolution images
Emiel Hoogeboom, Jonathan Heek, Tim Salimans
Currently, applying diffusion models in pixel space of high resolution images is difficult. Instead, existing approaches focus on diffusion in lower dimensional spaces (latent diff…
Efficiently Scaling Transformer Inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery +7
We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long seque…
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
Lasse Espeholt, Shreya Agrawal, Casper Sønderby +7
The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among…
MetNet: A Neural Weather Model for Precipitation Forecasting
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek +6
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuousl…
Bayesian Inference for Large Scale Image Classification
Jonathan Heek, Nal Kalchbrenner
Bayesian inference promises to ground and improve the performance of deep neural networks. It promises to be robust to overfitting, to simplify the training procedure and the space…