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Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine

arXiv cs.LG · 2026-05-21 Cached

This paper identifies a collapse-and-refine mechanism in diffusion models under the manifold hypothesis, proposing Score-induced Latent Diffusion (SiLD) that provably avoids the curse of dimensionality. Experiments show SiLD matches or outperforms VAE-based latent diffusion models.

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