Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

TL;DR

This paper introduces step-by-step preference optimization (SPO) for diffusion models, enhancing aesthetic quality by focusing on fine-grained visual differences without sacrificing alignment, and demonstrating faster convergence and superior aesthetics.

Contribution

The paper proposes a novel SPO method that improves aesthetic generation in diffusion models by using step-aware preferences and fine-grained supervision, outperforming existing DPO techniques.

Findings

SPO significantly improves aesthetic quality over existing DPO methods.

SPO converges faster than traditional DPO due to better preference labeling.

Models fine-tuned with SPO maintain strong image-text alignment.

Abstract

Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct preference optimization (DPO), which has been applied to diffusion models to improve general image quality including prompt alignment and aesthetics. Popular DPO methods propagate preference labels from clean image pairs to all the intermediate steps along the two generation trajectories. However, preference labels provided in existing datasets are blended with layout and aesthetic opinions, which would disagree with aesthetic preference. Even if aesthetic labels were provided (at substantial cost), it would be hard for the two-trajectory methods to capture nuanced visual differences at different steps. To improve aesthetics economically, this paper uses existing generic preference data and introduces step-by-step preference optimization (SPO) that discards the propagation strategy and allows fine-grained image details to be assessed. Specifically, at each denoising step, we 1) sample a pool of candidates by denoising from a shared noise latent, 2) use a step-aware preference model to find a suitable win-lose pair to supervise the diffusion model, and 3) randomly select one from the pool to initialize the next denoising step. This strategy ensures that diffusion models focus on the subtle, fine-grained visual differences instead of layout aspect. We find that aesthetics can be significantly enhanced by accumulating these improved minor differences. When fine-tuning Stable Diffusion v1.5 and SDXL, SPO yields significant improvements in aesthetics compared with existing DPO methods while not sacrificing image-text alignment compared with vanilla models. Moreover, SPO converges much faster than DPO methods due to the use of more correct preference labels provided by the step-aware preference model.