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arXiv · 2509.21375

Automated Prompt Generation for Creative and Counterfactual Text-to-image Synthesis

Aleksa Jelaca·Ying Jiao·Chang Tian·Marie-Francine Moens
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Abstract

Text-to-image generation has advanced rapidly with large-scale multimodal training, yet fine-grained controllability remains a critical challenge. Counterfactual controllability, defined as the capacity to deliberately generate images that contradict common-sense patterns, remains a major challenge but plays a crucial role in enabling creativity and exploratory applications. In this work, we address this gap with a focus on counterfactual size (e.g., generating a tiny walrus beside a giant button) and propose an automatic prompt engineering framework that adapts base prompts into revised prompts for counterfactual images. The framework comprises three components: an image evaluator that guides dataset construction by identifying successful image generations, a supervised prompt rewriter that produces revised prompts, and a DPO-trained ranker that selects the optimal revised prompt. We construct the first counterfactual size text-image dataset and enhance the image evaluator by extending Grounded SAM with refinements, achieving a 114 percent improvement over its backbone. Experiments demonstrate that our method outperforms state-of-the-art baselines and ChatGPT-4o, establishing a foundation for future research on counterfactual controllability.

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Aleksa Jelaca, Ying Jiao, Chang Tian, Marie-Francine Moens. 2025-09-23. Automated Prompt Generation for Creative and Counterfactual Text-to-image Synthesis. https://arxiv.org/abs/2509.21375

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