Diffusion models have been successful on a range of conditional generation
tasks including molecular design and text-to-image generation. However, these
achievements have primarily depended on task-specific conditional training or
error-prone heuristic approximations. Ideally, a conditional generation method
should provide exact samples for a broad range of conditional distributions
without requiring task-specific training. To this end, we introduce the Twisted
Diffusion Sampler, or TDS. TDS is a sequential Monte Carlo (SMC) algorithm that
targets the conditional distributions of diffusion models. The main idea is to
use twisting, an SMC technique that enjoys good computational efficiency, to
incorporate heuristic approximations without compromising asymptotic exactness.
We first find in simulation and on MNIST image inpainting and class-conditional
generation tasks that TDS provides a computational statistical trade-off,
yielding more accurate approximations with many particles but with empirical
improvements over heuristics with as few as two particles. We then turn to
motif-scaffolding, a core task in protein design, using a TDS extension to
Riemannian diffusion models. On benchmark test cases, TDS allows flexible
conditioning criteria and often outperforms the state of the art.

本文提出了一种名为 Twisted Diffusion Sampler 的串行蒙特卡罗算法用于生成条件分布，该算法具有更高的计算效率和更准确的近似度，并成功应用于蛋白质设计。