Core Concepts
Introducing a principled framework, Align Your Steps, to optimize the sampling schedule of diffusion models for high-quality outputs, especially in the few-step synthesis regime.
Abstract
The content discusses a novel framework called Align Your Steps (AYS) for optimizing the sampling schedules of diffusion models to improve the quality of generated outputs, especially in the few-step synthesis regime.
The key highlights are:
The authors demonstrate that the optimal sampling schedule depends on the characteristics of the dataset, and the commonly used heuristic schedules are suboptimal.
They propose AYS, a principled framework based on stochastic calculus, to optimize the sampling schedule specific to the dataset, model, and stochastic solver. This is done by minimizing an upper bound on the Kullback-Leibler divergence between the true and linearized generative SDEs.
The optimized schedules are evaluated on various image, video, and 2D toy data synthesis benchmarks, using a variety of different samplers. The results show that the AYS-optimized schedules outperform previous hand-crafted schedules in almost all experiments, especially in the low number of function evaluations (NFE) regime.
The authors provide the optimized schedules for several commonly used models in the appendix to allow for easy plug-and-play use by the research community.
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