Iterative Importance Fine-tuning of Diffusion Models

Abstract

Diffusion models are an important tool for generative modelling, serving as effective priors in applications such as imaging and protein design. A key challenge in applying diffusion models for downstream tasks is efficiently sampling from resulting posterior distributions, which can be addressed using the h-transform. This work introduces a self-supervised algorithm for fine-tuning diffusion models by estimating the h-transform, enabling amortised conditional sampling. Our method iteratively refines the h-transform using a synthetic dataset resampled with path-based importance weights. We demonstrate the effectiveness of this framework on class-conditional sampling and reward fine-tuning for text-to-image diffusion models.

Tasks

Reproductions