Automated Black-box Prompt Engineering For Personalized Text-to-image Generation

He Yutong, Robey Alexander, Murata Naoki, Jiang Yiding, Williams Joshua, Pappas George J., Hassani Hamed, Mitsufuji Yuki, Salakhutdinov Ruslan, Kolter J. Zico. Arxiv 2024

[Paper]    
In Context Learning Merging Prompting RAG Reinforcement Learning

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.

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