Forward-Value Discretization: Faster Stochastic Diffusion Sampling Without High-Order Approximation
[Uczenie maszynowe] Autorzy: Yuchen Jiao, Na Li, Changxiao Cai i 2 więcej
Stochastic diffusion samplers often require substantially more neural function evaluations (NFEs) than deterministic samplers to achieve comparable sample quality. Although higher-order discretization accelerates deterministic sampling, its benefits for stochastic sampling remains unclear. We establish matching upper and worst-case lower bounds showing that stochastic samplers with higher-order approximations remain first-order convergent, even with exact score functions. Thus, increasing the approximation order does not generally improve the convergence order. Motivated by this limitation, we introduce forward-value discretization, a training-free framework that evaluates the data/noise prediction model at a lookahead estimate constructed from previous predictions. For deterministic sampling, the resulting Forward DPMSolver (F-DPMSolver), improves sample quality at low NFE budgets thro…
- Kategoria arxiv
- cs.LG
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- Uczenie maszynowe