Tomorrow at #CVPR2026 Come join our full-day tutorial on analytic understanding of diffusion models. How do diffusion models generalize? What outputs can you expect from your generative model? Where should you direct resources to make it better? This and much more on a full day tutorial with wonderful @yuancy @MasonKamb @CScarvelis @WangBinxu @ZKadkhodaie Links in the reply.

Why do diffusion models produce new images instead of just memorizing the dataset? We show that they learn pixel correlation patterns from the data and therefore denoise locally, which promotes generalization. To test this idea, we compare trained diffusion models with a training-free algorithm that mixes local patches from the dataset. Surprisingly, this simple procedure already reproduces many properties of the trained models. 🧵 Check out this thread for more details about our Spotlight NeurIPS paper with @yuancy, @JustinMSolomon and @vincesitzmann.

At #NeurIPS today in San Diego? Come check out poster #4409 (4:30–7:30 PM) today. We’re excited to share our spotlight paper on the generalization properties of diffusion models. Looking forward to great research conversations! @yuancy @JustinMSolomon @vincesitzmann