  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.
