Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models

Understand

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.