Conditional Image Generation with Score-Based Diffusion Models

• URL: http://arxiv.org/abs/2111.13606v1
• Date: Fri, 26 Nov 2021 17:10:07 GMT
• Title: Conditional Image Generation with Score-Based Diffusion Models
• Authors: Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Sch\"onlieb, Christian Etmann
• Abstract summary: We conduct a systematic comparison and theoretical analysis of different approaches to learning conditional probability distributions with score-based diffusion models. We prove results which provide a theoretical justification for one of the most successful estimators of the conditional score. We introduce a multi-speed diffusion framework, which leads to a new estimator for the conditional score, performing on par with previous state-of-the-art approaches.
• Score: 1.1470070927586016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling. In this work we conduct a systematic comparison and theoretical analysis of different approaches to learning conditional probability distributions with score-based diffusion models. In particular, we prove results which provide a theoretical justification for one of the most successful estimators of the conditional score. Moreover, we introduce a multi-speed diffusion framework, which leads to a new estimator for the conditional score, performing on par with previous state-of-the-art approaches. Our theoretical and experimental findings are accompanied by an open source library MSDiff which allows for application and further research of multi-speed diffusion models.

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