Scalable Ranked Preference Optimization for Text-to-Image Generation
Authors: name: Shyamgopal Karthik, name: Huseyin Coskun, name: Zeynep Akata, name: Sergey Tulyakov, name: Jian Ren, name: Anil Kag

Direct Preference Optimization (DPO) has emerged as a powerful approach to
align text-to-image (T2I) models with human feedback. Unfortunately, successful
application of DPO to T2I models requires a huge amount of resources to collect
and label large-scale datasets, e.g., millions of generated paired images
annotated with human preferences. In addition, these human preference datasets
can get outdated quickly as the rapid improvements of T2I models lead to higher
quality images. In this work, we investigate a scalable approach for collecting
large-scale and fully synthetic datasets for DPO training. Specifically, the
preferences for paired images are generated using a pre-trained reward
function, eliminating the need for involving humans in the annotation process,
greatly improving the dataset collection efficiency. Moreover, we demonstrate
that such datasets allow averaging predictions across multiple models and
collecting ranked preferences as opposed to pairwise preferences. Furthermore,
we introduce RankDPO to enhance DPO-based methods using the ranking feedback.
Applying RankDPO on SDXL and SD3-Medium models with our synthetically generated
preference dataset ``Syn-Pic'' improves both prompt-following (on benchmarks
like T2I-Compbench, GenEval, and DPG-Bench) and visual quality (through user
studies). This pipeline presents a practical and scalable solution to develop
better preference datasets to enhance the performance of text-to-image models.