PromptMap: Supporting Exploratory Text-to-Image Generation
Yuhan Guo, Xingyou Liu, Xiaoru Yuan, Kai Xu
TL;DR
PromptMap addresses the challenge of disorientation in exploratory text-to-image generation by introducing the Design-Exploration model and a node-link visualization that captures nonlinear thinking and structured subspaces. The system represents exploration as a dynamic interplay between prompts (designs) and subspaces (structured dimensions) using recursive grids, enabling easy history review, comparison across dimensions, and curated reuse of results. Through eight qualitative interviews, PromptMap is shown to support organized, divergent exploration and sensemaking, with participants valuing the explicit representation of thinking paths and the ability to manage large design spaces. This approach offers practical benefits for artists and designers by reducing cognitive load and providing a scalable, flexible workflow for open-ended creative exploration, with potential for image-to-image integration and personalized recommendations in future work.
Abstract
Text-to-image generative models can be tremendously valuable in supporting creative tasks by providing inspirations and enabling quick exploration of different design ideas. However, one common challenge is that users may still not be able to find anything useful after many hours and hundreds of images. Without effective help, users can easily get lost in the vast design space, forgetting what has been tried and what has not. In this work, we first propose the Design-Exploration model to formalize the exploration process. Based on this model, we create an interactive visualization system, PromptMap, to support exploratory text-to-image generation. Our system provides a new visual representation that better matches the non-linear nature of such processes, making them easier to understand and follow. It utilizes novel visual representations and intuitive interactions to help users structure the many possibilities that they can explore. We evaluated the system through in-depth interviews with users.
