AI RESEARCH
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
arXiv CS.LG
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ArXi:2606.03820v1 Announce Type: cross We develop a quantitative approximation framework for diffusion distillation, viewing few-step sampling as error propagation under compositions of learned flow maps. Focusing on trajectory distillation for the probability-flow ODE, we show that local approximation errors can be strongly amplified in low-noise multimodal regimes, where the underlying dynamics become stiff.