Abstract

Generative models are not immune to spurious correlations. The spuriousness in generative models is defined by their ability to compose attributes faithfully, often referred to as compositionality in generative models. To compose attributes successfully, a model should learn to accurately capture the statistical independence between attributes. This paper shows that standard conditional diffusion models violate this assumption, even when all attribute compositions are observed during training. And, this violation is significantly more severe when only a subset of the compositions is observed. We propose CoInD to address this problem. It explicitly enforces statistical independence between the conditional marginal distributions by minimizing Fisher’s divergence between the joint and marginal distributions. The theoretical advantages of CoInD are reflected in both qualitative and quantitative experiments, demonstrating a significantly more faithful and precisely controlled generation of samples for arbitrary compositions of attributes.