In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sampled multiple times to obtain a diverse set incurring a cost that is orthogonal to sampling time. We tackle the question of how to improve diversity and sample efficiency by moving beyond the common assumption of independent samples. We propose particle guidance, an extension of diffusion-based generative sampling where a joint-particle time-evolving potential enforces diversity. We analyze theoretically the joint distribution that particle guidance generates, how to learn a potential that achieves optimal diversity, and the connections with methods in other disciplines. Empirically, we test the framework both in the setting of conditional image generation, where we are able to increase diversity without affecting quality, and molecular conformer generation, where we reduce the state-of-the-art median error by 13% on average.

翻译：鉴于生成模型的广泛成功，大量研究致力于加快其采样速度。然而，生成模型通常需要多次采样以获得多样化结果，这一过程的成本与采样时间正交。我们通过突破独立样本的常见假设，探索如何提高多样性与采样效率。本文提出粒子引导方法，即扩散生成采样的扩展技术，通过联合粒子时变势能强制实现多样性。我们从理论上分析了粒子引导生成的联合分布、如何学习实现最优多样性的势能函数，以及该方法与其他学科方法的关联。实验方面，我们分别在条件图像生成（在保证质量的前提下提升多样性）和分子构象生成（平均将最新技术的中间误差降低13%）两种场景中测试了该框架。