AI RESEARCH
Stochastic Transition-Map Distillation for Fast Probabilistic Inference
arXiv CS.LG
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ArXi:2605.07661v1 Announce Type: new Diffusion models achieve strong generation quality, diversity, and distribution coverage, but their performance often comes with expensive inference. In this work, we propose Stochastic Transition-Map Distillation (STMD), a teacher-free framework for accelerating diffusion model inference while preserving probabilistic sample generation.