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Explore novel training-free algorithms designed to accelerate the convergence of score-based diffusion models in this 40-minute mathematical conference talk. Learn about the theoretical foundations behind acceleration techniques for both deterministic DDIM and stochastic DDPM samplers, addressing the critical challenge of slow sampling speeds that plague diffusion models despite their remarkable empirical performance. Discover how extensive function evaluations during the sampling phase create bottlenecks and examine cutting-edge solutions that maintain model quality while significantly reducing computational requirements. Gain insights into the mathematical underpinnings that have been severely limited in existing acceleration approaches, and understand how these new algorithms can be implemented without requiring model retraining. The presentation was recorded during the "Meeting in Mathematical Statistics" thematic meeting at the Centre International de Rencontres Mathématiques in Marseille, France, providing access to advanced research in mathematical statistics and machine learning theory.
