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, its implications on the choice of potential,
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.

通过提出一种名为 “粒子引导” 的扩展扩散式生成抽样方法，我们在研究中探索了如何提高生成模型的多样性和采样效率，并通过在条件图像生成和分子构象生成中的实证测试，展示了这一方法的价值。