all AI news
Multistep Consistency Models
March 12, 2024, 4:42 a.m. | Jonathan Heek, Emiel Hoogeboom, Tim Salimans
cs.LG updates on arXiv.org arxiv.org
Abstract: 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.
In this paper we propose Multistep Consistency Models: A unification between Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that can interpolate between a consistency model and a diffusion model: a trade-off between sampling speed and sampling quality. Specifically, a 1-step consistency …
abstract arxiv cs.cv cs.lg diffusion diffusion models easy generate paper samples song stat.ml train type unification
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist
@ Meta | Menlo Park, CA
Principal Data Scientist
@ Mastercard | O'Fallon, Missouri (Main Campus)