124 citations · 499 across the 26 of their papers we have counts for
5 papers · 1 filter
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…
Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations
Manoj Kumar, Neil Houlsby, Emiel Hoogeboom
Generating image variations, where a model produces variations of an input image while preserving the semantic context has gained increasing attention. Current image variation tech…
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.…