Automated Black-box Prompt Engineering for Personalized Text-to-Image
  Generation
Yutong He Alexander Robey Naoki Murata Yiding Jiang Joshua Williams George J. Pappas Hamed Hassani Yuki Mitsufuji Ruslan Salakhutdinov J. Zico Kolter
Prompt engineering is effective for controlling the output of text-to-image
(T2I) generative models, but it is also laborious due to the need for manually
crafted prompts. This challenge has spurred the development of algorithms for
automated prompt generation. However, these methods often struggle with
transferability across T2I models, require white-box access to the underlying
model, and produce non-intuitive prompts. In this work, we introduce PRISM, an
algorithm that automatically identifies human-interpretable and transferable
prompts that can effectively generate desired concepts given only black-box
access to T2I models. Inspired by large language model (LLM) jailbreaking,
PRISM leverages the in-context learning ability of LLMs to iteratively refine
the candidate prompts distribution for given reference images. Our experiments
demonstrate the versatility and effectiveness of PRISM in generating accurate
prompts for objects, styles and images across multiple T2I models, including
Stable Diffusion, DALL-E, and Midjourney.