Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization

Understand

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.