The Joy of Co-Painting: Creative Human-AI Collaboration for Traceable Image-Generation Workflows

Image-generative models have gained popularity over the last years with their ability to create realistic artwork. Realizing complex artworks with specific creative ideas often requires iterative optimization of specialized prompts, but may still result in inadequate images. The inclusion of reference images and adapting modelspecific parameters can help in steering the model and fostering the creative intent of the user. But by providing text prompts, initial images, and adapting model parameters, users face a vast design space for creating images. To navigate through this space, we propose a visualization approach that combines an interactive Provenance Graph, parameter visualizations, and high-dimensional embeddings. Our approach helps pursue multiple parallel creation paths, makes workflows traceable and parameter changes transparent, and facilitates the reporting of image editing steps. In addition to prompt formulation, we focus on targeted generation by probing parameters, image compositions, and editing details. We integrate the generative process into existing image editing software, enabling users to compose artwork in collaboration with the model. The presented approach is evaluated in a user experiment ($\mathrm{n}=9$) for generating artwork. The results show that users with different levels of experience can create targeted artwork but use different strategies when working with the Provenance Graph.