Margin-aware Preference Optimization for Aligning Diffusion Models
  without Reference
Authors: name: Jiwoo Hong, name: Sayak Paul, name: Noah Lee, name: Kashif Rasul, name: James Thorne, name: Jongheon Jeong

Modern alignment techniques based on human preferences, such as RLHF and DPO,
typically employ divergence regularization relative to the reference model to
ensure training stability. However, this often limits the flexibility of models
during alignment, especially when there is a clear distributional discrepancy
between the preference data and the reference model. In this paper, we focus on
the alignment of recent text-to-image diffusion models, such as Stable
Diffusion XL (SDXL), and find that this "reference mismatch" is indeed a
significant problem in aligning these models due to the unstructured nature of
visual modalities: e.g., a preference for a particular stylistic aspect can
easily induce such a discrepancy. Motivated by this observation, we propose a
novel and memory-friendly preference alignment method for diffusion models that
does not depend on any reference model, coined margin-aware preference
optimization (MaPO). MaPO jointly maximizes the likelihood margin between the
preferred and dispreferred image sets and the likelihood of the preferred sets,
simultaneously learning general stylistic features and preferences. For
evaluation, we introduce two new pairwise preference datasets, which comprise
self-generated image pairs from SDXL, Pick-Style and Pick-Safety, simulating
diverse scenarios of reference mismatch. Our experiments validate that MaPO can
significantly improve alignment on Pick-Style and Pick-Safety and general
preference alignment when used with Pick-a-Pic v2, surpassing the base SDXL and
other existing methods. Our code, models, and datasets are publicly available
via https://mapo-t2i.github.io