Vanilla Diffusion Models Collapse on Out‑of‑Distribution Compositional Tasks
Vanilla conditional diffusion models collapse when asked to generate samples from compositional distributions that lie outside their training support. The authors prove this failure theoretically and confirm it with synthetic and realistic experiments, showing that inference‑time corrections cannot overcome the fundamental score‑estimation error. This reveals a core obstacle to compositional generalization in generative AI.