Free Lunch Alignment of Text-to-Image Diffusion Models without
  Preference Image Pairs
Authors: name: Jia Jun Cheng Xian, name: Muchen Li, name: Haotian Yang, name: Xin Tao, name: Pengfei Wan, name: Leonid Sigal, name: Renjie Liao
Organization: University of British Columbia

Recent advances in diffusion-based text-to-image (T2I) models have led to
remarkable success in generating high-quality images from textual prompts.
However, ensuring accurate alignment between the text and the generated image
remains a significant challenge for state-of-the-art diffusion models. To
address this, existing studies employ reinforcement learning with human
feedback (RLHF) to align T2I outputs with human preferences. These methods,
however, either rely directly on paired image preference data or require a
learned reward function, both of which depend heavily on costly, high-quality
human annotations and thus face scalability limitations. In this work, we
introduce Text Preference Optimization (TPO), a framework that enables
"free-lunch" alignment of T2I models, achieving alignment without the need for
paired image preference data. TPO works by training the model to prefer matched
prompts over mismatched prompts, which are constructed by perturbing original
captions using a large language model. Our framework is general and compatible
with existing preference-based algorithms. We extend both DPO and KTO to our
setting, resulting in TDPO and TKTO. Quantitative and qualitative evaluations
across multiple benchmarks show that our methods consistently outperform their
original counterparts, delivering better human preference scores and improved
text-to-image alignment. Our Open-source code is available at
https://github.com/DSL-Lab/T2I-Free-Lunch-Alignment.