Divergence Minimization Preference Optimization for Diffusion Model Alignment
This work addresses alignment challenges in diffusion models for generating images from text prompts, offering a robust method with theoretical grounding, though it appears incremental as it builds on existing preference optimization frameworks.
The paper tackles the problem of aligning diffusion models with human preferences by introducing Divergence Minimization Preference Optimization (DMPO), which minimizes reverse KL divergence to avoid suboptimal mean-seeking optimization. The results show that DMPO consistently outperforms or matches existing techniques across different base models and test sets, achieving the best PickScore in every case.
Diffusion models have achieved remarkable success in generating realistic and versatile images from text prompts. Inspired by the recent advancements of language models, there is an increasing interest in further improving the models by aligning with human preferences. However, we investigate alignment from a divergence minimization perspective and reveal that existing preference optimization methods are typically trapped in suboptimal mean-seeking optimization. In this paper, we introduce Divergence Minimization Preference Optimization (DMPO), a novel and principled method for aligning diffusion models by minimizing reverse KL divergence, which asymptotically enjoys the same optimization direction as original RL. We provide rigorous analysis to justify the effectiveness of DMPO and conduct comprehensive experiments to validate its empirical strength across both human evaluations and automatic metrics. Our extensive results show that diffusion models fine-tuned with DMPO can consistently outperform or match existing techniques, specifically consistently outperforming all baseline models across different base models and test sets, achieving the best PickScore in every case, demonstrating the method's superiority in aligning generative behavior with desired outputs. Overall, DMPO unlocks a robust and elegant pathway for preference alignment, bridging principled theory with practical performance in diffusion models.