From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime
How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are based largely on heuristics or empirical tuning. Here, we develop a general statistical framework for studying asymptotically optimal noise-level allocation in diffusion training. Our first main result concerns the fully coupled regime, where information can spread between different time points. Under convexity or Polyak-Lojasiewicz-type assumptions, we show that the optimized training schedule admits an atomic minimizer, concentrated on finitely many noise levels. Our second main result specializes this framework to an idealized independent-...
| Authors | Luca Ambrogioni, Giulio Franzese, Alberto Foresti et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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