{"type":"ScholarlyArticle","title":"Manipulating Embeddings of Stable Diffusion Prompts","authors":[{"name":"Niklas Deckers","slug":"niklas-deckers","given":"Niklas","family":"Deckers","affiliation":"Leipzig University; ScaDS."},{"name":"Julia Peters","slug":"julia-peters","given":"Julia","family":"Peters","affiliation":"Leipzig University; ScaDS."},{"name":"Martin Potthast","slug":"martin-potthast","given":"Martin","family":"Potthast","affiliation":"ScaDS.; University of Kassel; hessian.AI"}],"abstract":"Prompt engineering is still the primary way for users of generative text-to-image models to manipulate generated images in a targeted way. Based on treating the model as a continuous function and by passing gradients between the image space and the prompt embedding space, we propose and analyze a new method to directly manipulate the embedding of a prompt instead of the prompt text. We then derive three practical interaction tools to support users with image generation: (1) Optimization of a metric defined in the image space that measures, for example, the image style. (2) Supporting a user in creative tasks by allowing them to navigate in the image space along a selection of directions of \"near\" prompt embeddings. (3) Changing the embedding of the prompt to include information that a user has seen in a particular seed but has difficulty describing in the prompt. Compared to prompt engineering, user-driven prompt embedding manipulation enables a more fine-grained, targeted control that integrates a user's intentions. Our user study shows that our methods are considered less tedious and that the resulting images are often preferred.","doi":"10.24963/ijcai.2024/845","url":"https://ijcai.org/proceedings/2024/845","pdf":"https://ijcai.org/proceedings/2024/0845.pdf","markdown":"https://ijcai.org/proceedings/2024/845.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/845","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":845,"pages":{"first":7636,"last":7644},"keywords":["Application domains: Images, movies and visual arts","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning"],"videos":[],"references":[{"position":1,"raw":"[Balaji et al., 2022] Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu. ediff-i: Text-to-image diffu- sion models with an ensemble of expert denoisers. 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