PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation

TL;DR

PROUD is a diffusion model that generates high-quality samples satisfying multiple conflicting properties by optimizing for Pareto optimality, balancing trade-offs effectively.

Contribution

It introduces a novel constrained optimization framework and a diffusion model that dynamically adjusts gradients to achieve Pareto optimality without sacrificing sample quality.

Findings

Outperforms baselines in image and protein generation tasks.

Maintains superior sample quality while approaching Pareto front.

Effectively balances multiple property objectives.

Abstract

Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these property functions independently, thus omitting the trade-offs among them. In addition, the property optimization is often improperly integrated into the generative models, resulting in an unnecessary compromise on generation quality (i.e., the quality of generated samples). To address these issues, we formulate a constrained optimization problem. It seeks to optimize generation quality while ensuring that generated samples reside at the Pareto front of multiple property objectives. Such a formulation enables the generation of samples that cannot be further improved simultaneously on the conflicting property functions and preserves good quality of generated samples. Building upon this formulation, we introduce the PaRetO-gUided Diffusion model (PROUD), wherein the gradients in the denoising process are dynamically adjusted to enhance generation quality while the generated samples adhere to Pareto optimality. Experimental evaluations on image generation and protein generation tasks demonstrate that our PROUD consistently maintains superior generation quality while approaching Pareto optimality across multiple property functions compared to various baselines.