SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models

TL;DR

SIPO introduces a stabilized and improved preference optimization framework that enhances the alignment of diffusion models with human preferences by addressing training instability and off-policy bias.

Contribution

The paper proposes SIPO, a novel framework with gradient clipping and timestep-aware importance reweighting, to improve stability and effectiveness in preference-based diffusion model alignment.

Findings

SIPO stabilizes training across various diffusion models.

Outperforms existing alignment methods on multiple benchmarks.

Provides guidelines for timestep-aware preference optimization.

Abstract

Preference learning has garnered extensive attention as an effective technique for aligning diffusion models with human preferences in visual generation. However, existing alignment approaches such as Diffusion-DPO suffer from two fundamental challenges: training instability caused by high gradient variances at various timesteps and high parameter sensitivities, and off-policy bias arising from the discrepancy between the optimization data and the policy models' distribution. Our first contribution is a systematic analysis of diffusion trajectories across different timesteps, identifying that the instability primarily originates from early timesteps with low importance weights. To address these issues, we propose \textbf{SIPO}, a \textbf{S}tabilized and \textbf{I}mproved \textbf{P}reference \textbf{O}ptimization framework for aligning diffusion models with human preferences. Concretely, a key gradient, \emph{i.e.,} DPO-C\&M is introduced to stabilize training by clipping and masking uninformative timesteps. This is followed by a timestep-aware importance-reweighting paradigm to mitigate off-policy bias and emphasize informative updates throughout the alignment process. Extensive experiments on various baseline models including image generation models on SD1.5, SDXL, and video generation models CogVideoX-2B/5B, Wan2.1-1.3B, demonstrate that our SIPO consistently promotes stabilized training and outperforms existing alignment methods that with meticulous adjustments on parameters.Overall, these results suggest the importance of timestep-aware alignment and provide valuable guidelines for improved preference optimization in aligning diffusion models.