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

arXiv: https://arxiv.org/abs/2403.09615 · HTML: https://mlsanity.com/paper/2403.09615/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 11 of 11)

- [Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets](https://mlsanity.com/paper/2412.16839/) (2024)
- [ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map](https://mlsanity.com/paper/2407.12315/) (2024)
- [Exploring Empty Spaces: Human-in-the-Loop Data Augmentation](https://mlsanity.com/paper/2410.01088/) (2024)
- [SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches](https://mlsanity.com/paper/2502.07556/) (2025)
- [Refer to Any Segmentation Mask Group With Vision-Language Prompts](https://mlsanity.com/paper/2506.05342/) (2025)
- [GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design](https://mlsanity.com/paper/2508.15227/) (2025)
- [Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users](https://mlsanity.com/paper/2602.16930/) (2026)
- [GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization](https://mlsanity.com/paper/2601.18585/) (2026)
- [Adaptive Prompt Elicitation for Text-to-Image Generation](https://mlsanity.com/paper/2602.04713/) (2026)
- [Bridging the Intention-Expression Gap: Aligning Multi-Dimensional Preferences via Hierarchical Relevance Feedback in Text-to-Image Diffusion](https://mlsanity.com/paper/2603.14936/) (2026)
- [Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration](https://mlsanity.com/paper/2607.02563/) (2026)

## Abstract (arXiv)

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 it often requires many trials and errors to achieve satisfactory results. The prompt history contains a wealth of information that could provide users with insights into what have 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. Besides Image Variant Graph, PrompTHis also incorporates a detailed prompt-image history and a navigation mini-map. 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 with eleven amateur participants and qualitative interviews with five professionals and one amateur user were conducted to evaluate the effectiveness of PrompTHis. The results demonstrate PrompTHis can help users review the prompt history, make sense of the model, and plan their creative process.