Iterative Importance Fine-tuning of Diffusion Models

Alexander Denker; Shreyas Padhy; Francisco Vargas; Johannes Hertrich

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arXiv · 2502.04468

Iterative Importance Fine-tuning of Diffusion Models

Alexander Denker·Shreyas Padhy·Francisco Vargas·Johannes Hertrich

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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 Doob's $h$-transform. This work introduces a self-supervised algorithm for fine-tuning diffusion models by learning the optimal control, enabling amortised conditional sampling. Our method iteratively refines the control 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.

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Alexander Denker, Shreyas Padhy, Francisco Vargas, Johannes Hertrich. 2025-02-06. Iterative Importance Fine-tuning of Diffusion Models. https://arxiv.org/abs/2502.04468

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