Local Mechanisms of Compositional Generalization in Conditional Diffusion

Understand

Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying this ability remain unclear.

• To make this concrete, we study length generalization, the ability to generate images with more objects than seen during training.
• In a controlled CLEVR setting (Johnson et al., 2017), we find that length generalization is achievable in some cases but not others, suggesting that models only sometimes learn the underlying compositional structure.
• We then investigate locality as a structural mechanism for compositional generalization.