  Text-to-Image (T2I) diffusion models have achieved remarkable success in
image generation. Despite their progress, challenges remain in both
prompt-following ability, image quality and lack of high-quality datasets,
which are essential for refining these models. As acquiring labeled data is
costly, we introduce AGFSync, a framework that enhances T2I diffusion models
through Direct Preference Optimization (DPO) in a fully AI-driven approach.
AGFSync utilizes Vision-Language Models (VLM) to assess image quality across
style, coherence, and aesthetics, generating feedback data within an AI-driven
loop. By applying AGFSync to leading T2I models such as SD v1.4, v1.5, and
SDXL, our extensive experiments on the TIFA dataset demonstrate notable
improvements in VQA scores, aesthetic evaluations, and performance on the HPSv2
benchmark, consistently outperforming the base models. AGFSync's method of
refining T2I diffusion models paves the way for scalable alignment techniques.
