We argue that generative text-to-image models often struggle with prompt adherence due to the noisy and unstructured nature of large-scale datasets like LAION-5B. This forces users to rely heavily on prompt engineering to elicit desirable outputs. In this work, we propose that enforcing a consistent caption structure during training can significantly improve model controllability and alignment. We introduce Re-LAION-Caption 19M, a high-quality subset of Re-LAION-5B, comprising 19 million 1024x1024 images with captions generated by a Mistral 7B Instruct-based LLaVA-Next model. Each caption follows a four-part template: subject, setting, aesthetics, and camera details. We fine-tune PixArt-Σ and Stable Diffusion 2 using both structured and randomly shuffled captions, and show that structured versions consistently yield higher text-image alignment scores using visual question answering (VQA) models. The dataset is publicly available at https://huggingface.co/datasets/supermodelresearch/Re-LAION-Caption19M.
@article{arxiv.2507.05300,
title = {Structured Captions Improve Prompt Adherence in Text-to-Image Models (Re-LAION-Caption 19M)},
author = {Nicholas Merchant and Haitz Sáez de Ocáriz Borde and Andrei Cristian Popescu and Carlos Garcia Jurado Suarez},
journal= {arXiv preprint arXiv:2507.05300},
year = {2025}
}