PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation

Guo, Yuhan; Shao, Hanning; Liu, Can; Xu, Kai; Yuan, Xiaoru

Abstract

Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most users have a limited understanding of how such models work and often rely on trial and error strategies to achieve satisfactory results. The prompt history contains a wealth of information that could provide users with insights into what has been explored and how the prompt changes impact the output image, yet little research attention has been paid to the visual analysis of such process to support users. We propose the Image Variant Graph, a novel visual representation designed to support comparing prompt-image pairs and exploring the editing history. The Image Variant Graph models prompt differences as edges between corresponding images and presents the distances between images through projection. Based on the graph, we developed the PrompTHis system through co-design with artists. Based on the review and analysis of the prompting history, users can better understand the impact of prompt changes and have a more effective control of image generation. A quantitative user study and qualitative interviews demonstrate that PrompTHis can help users review the prompt history, make sense of the model, and plan their creative process.

Citation

Guo, Y., Shao, H., Liu, C., Xu, K., & Yuan, X. (2024). PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation. IEEE Transactions on Visualization and Computer Graphics, https://doi.org/10.1109/TVCG.2024.3408255

• Journal Article Type: Article
• Acceptance Date: Nov 1, 2023
• Online Publication Date: Jun 3, 2024
• Publication Date: Jun 3, 2024
• Deposit Date: Jan 22, 2025
• Publicly Available Date: Jan 29, 2025
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Print ISSN: 1077-2626
• Electronic ISSN: 1941-0506
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TVCG.2024.3408255
• Public URL: https://nottingham-repository.worktribe.com/output/44425503
• Publisher URL: https://ieeexplore.ieee.org/document/10546311