Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate

The paper presents a critical analysis of vanilla conditional diffusion models in the context of compositional generation, arguing that they struggle to extrapolate to target distributions defined by combinations of source distributions. The authors provide theoretical insights and experimental evidence indicating that score estimation errors significantly hinder performance, particularly when dealing with out-of-distribution targets, thus suggesting the necessity for alternative methodologies. This work is relevant for AI practitioners as it highlights the limitations of current diffusion models and the need for improved approaches in generative tasks involving unseen combinations of data.