Analyzing distillation methods to reduce diffusion model sampling steps

Diffusion models achieve high-quality results through iterative denoising, but distillation techniques allow student models to mimic teacher predictions in fewer steps. This reduction in sampling steps addresses the computational bottleneck of sequential refinement in high-dimensional distributions.