Authors
- Yutong He
- Alexander Robey
- Naoki Murata
- Yiding Jiang
- Joshua Williams
- George J. Pappas
- Hamed Hassani
- Yuki Mitsufuji
- Ruslan Salakhutdinov*
- J. Zico Kolter*
* External authors
Venue
- NeurIPS-24
Date
- 2025
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Yutong He
Alexander Robey
Yiding Jiang
Joshua Williams
George J. Pappas
Hamed Hassani
Ruslan Salakhutdinov*
J. Zico Kolter*
* External authors
NeurIPS-24
2025
Abstract
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.
Related Publications
We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare…
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific mod…
Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thu…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.



