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arXiv · 2308.12059

Manipulating Embeddings of Stable Diffusion Prompts

Niklas Deckers·Julia Peters·Martin Potthast
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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.

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Niklas Deckers, Julia Peters, Martin Potthast. 2023-08-23. Manipulating Embeddings of Stable Diffusion Prompts. https://arxiv.org/abs/2308.12059

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