282 citations · 295 across the 6 of their papers we have counts for
6 papers
Rolling Diffusion Models
David Ruhe, Jonathan Heek, Tim Salimans +1
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent fram…
Blurring Diffusion Models
Emiel Hoogeboom, Tim Salimans
Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaus…
Video Diffusion Models
Jonathan Ho, Tim Salimans, Alexey Gritsenko +3
Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model…
Palette: Image-to-Image Diffusion Models
Chitwan Saharia, William Chan, Huiwen Chang +5
This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image transla…
Autoregressive Diffusion Models
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings +3
We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete di…
Variational Diffusion Models
Diederik P. Kingma, Tim Salimans, Ben Poole +1
Diffusion-based generative models have demonstrated a capacity for perceptually impressive synthesis, but can they also be great likelihood-based models? We answer this in the affi…