Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization
This addresses the problem of integrating human preference learning into text-to-image generation for improved model alignment, representing an incremental advance in a relatively new area.
The paper tackles aligning text-to-image diffusion models with human preferences by introducing Diffusion-RPO, which outperforms existing methods like Supervised Fine-Tuning and Diffusion-DPO on Stable Diffusion versions, achieving superior results in automated human preference evaluations and a new style alignment metric.
Aligning large language models with human preferences has emerged as a critical focus in language modeling research. Yet, integrating preference learning into Text-to-Image (T2I) generative models is still relatively uncharted territory. The Diffusion-DPO technique made initial strides by employing pairwise preference learning in diffusion models tailored for specific text prompts. We introduce Diffusion-RPO, a new method designed to align diffusion-based T2I models with human preferences more effectively. This approach leverages both prompt-image pairs with identical prompts and those with semantically related content across various modalities. Furthermore, we have developed a new evaluation metric, style alignment, aimed at overcoming the challenges of high costs, low reproducibility, and limited interpretability prevalent in current evaluations of human preference alignment. Our findings demonstrate that Diffusion-RPO outperforms established methods such as Supervised Fine-Tuning and Diffusion-DPO in tuning Stable Diffusion versions 1.5 and XL-1.0, achieving superior results in both automated evaluations of human preferences and style alignment. Our code is available at https://github.com/yigu1008/Diffusion-RPO