We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.

翻译：本研究探讨了训练扩散模型以从具有给定未归一化密度或能量函数的分布中采样的方法。我们对多种扩散结构推理方法进行了基准测试，包括基于模拟的变分方法和离策略方法（连续生成流网络）。我们的结果揭示了现有算法的相对优势，同时对以往研究中的某些主张提出了质疑。我们还提出了一种新颖的离策略探索策略，该策略基于目标空间的局部搜索并利用回放缓冲区，实验表明该策略在多种目标分布上提升了采样质量。我们在 https://github.com/GFNOrg/gfn-diffusion 公开了所研究的采样方法与基准测试代码，为未来基于扩散模型的摊销推理研究提供基础。