Title: Iterative Importance Fine-tuning of Diffusion Models
Primary area: generative models
Keywords: Diffusion models, Conditional sampling, Fine-tuning, Generative modeling, Inverse problems, Stochastic control

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, inverse problems and reward fine-tuning for text-to-image diffusion models.

TL;DR: An iterative refinement framework for fine-tuning diffusion models using path-based importance-weighted resampling without requiring additional datasets