Hierarchically branched diffusion models for efficient and interpretable multi-class conditional generation

Diffusion models have achieved justifiable popularity by attaining state-of-the-art performance in generating realistic objects, including when conditioning generation on labels. Current diffusion models are universally linear in nature, modeling diffusion identically for objects of all classes. For the multi-class conditional generation problem, we propose a novel, structurally unique framework of diffusion models which are hierarchically branched according to the inherent relationships between classes. In this work, we showcase several advantages of branched diffusion models. We demonstrate that branched models generate samples more efficiently, and are more easily extended to novel classes in a continual-learning setting. We also show that branched models enjoy a unique interpretability that offers insight into the modeled data distribution. Branched diffusion models represent an alternative paradigm to their traditional linear counterparts, and can have large impacts in how we use diffusion models for efficient generation, online learning, and scientific discovery.