Researchers propose a new diffusion model sampling technique called speculative draft trees, which accelerates model generation by drafting candidate states and correcting them under a coupling that preserves the target distribution. This approach can reduce the number of expensive target evaluations, with experiments showing up to 8.3% acceleration over a baseline method.