Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance
Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Duan Li, Jian Gao, Li Zhang, Hao Yang, Boxing Chen, Osamu Yoshie

TL;DR
This paper introduces DialPrompt, a dialogue-based prompt generation system for text-to-image synthesis that enhances user control and understanding, especially for novices, by guiding multi-turn interactions based on essential prompt dimensions.
Contribution
It presents a novel multi-turn, user-centric prompt generation framework for TIS, curated from expert insights, improving novice user experience and interpretability.
Findings
Significant improvement in user-centricity scores.
Maintains high-quality image synthesis results.
Highly rated by novice users in evaluations.
Abstract
The emergence of text-to-image synthesis (TIS) models has significantly influenced digital image creation by producing high-quality visuals from written descriptions. Yet these models are sensitive on textual prompts, posing a challenge for novice users who may not be familiar with TIS prompt writing. Existing solutions relieve this via automatic prompt expansion or generation from a user query. However, this single-turn manner suffers from limited user-centricity in terms of result interpretability and user interactivity. Thus, we propose DialPrompt, a dialogue-based TIS prompt generation model that emphasizes user experience for novice users. DialPrompt is designed to follow a multi-turn workflow, where in each round of dialogue the model guides user to express their preferences on possible optimization dimensions before generating the final TIS prompt. To achieve this, we mined 15…
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Code & Models
Videos
Taxonomy
TopicsHandwritten Text Recognition Techniques · Human Motion and Animation · Video Analysis and Summarization
