PromptNavi: Text-to-image generation through interactive prompt visual exploration
Bofei Huang, Haoran Xie · Computers & Graphics · 2025
Modern text-to-image generative models can create high-quality and impressive images, but require extensive trial-and-error to interpret user intents. To solve this issue, we propose PromptNavi, a visual exploration interface for node-based prompt composition leveraging large language models to enhance the efficiency of text-to-image generation. In contrast to conventional prompting interfaces, PromptNavi allows users to manipulate and combine visual attributes of target images directly to refine outputs iteratively. Our user study confirmed that the results generated using PromptNavi achieved significant improvements in user usability, reduced cognitive load, and superior image quality rated by independent evaluators. It is verified that users achieved better results with less effort across all measured dimensions, including creativity, atmosphere, coherence, and overall impression. We believe PromptNavi may bridge the gap between user intent and generative AI outputs, advancing human-centered generative AI by making generative models accessible to novices with an enhanced user experience. Source codes are available at: https://anonymous.4open.science/r/project-5996/ . • We propose PromptNavi, a node-and-connection interface that renders prompt engineering more transparent, particularly for non-experts. • An LLM-based approach for fine-grained attribute interpolation, streamlining the process of refining prompts while clarifying the relationship between textual elements and generated outputs. • Empirical validation of PromptNavi’s effectiveness, including user studies demonstrating significant improve- ments in user experience and generative quality over existing baseline tools.