Authors

* External authors

Venue

Date

Share

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*

* 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

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

Interspeech, 2025
Yigitcan Özer, Woosung Choi, Joan Serrà, Mayank Kumar Singh*, Wei-Hsiang Liao, Yuki Mitsufuji

We introduce the Robust Audio Watermarking Benchmark (RAW-Bench), a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic comparisons. To simulate real-world usage, we introduce a comprehensive audio attack pipeline with var…

Training Consistency Models with Variational Noise Coupling

ICML, 2025
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji

Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and impr…

Supervised Contrastive Learning from Weakly-labeled Audio Segments for Musical Version Matching

ICML, 2025
Joan Serrà, R. Oguz Araz, Dmitry Bogdanov, Yuki Mitsufuji

Detecting musical versions (different renditions of the same piece) is a challenging task with important applications. Because of the ground truth nature, existing approaches match musical versions at the track level (e.g., whole song). However, most applications require to …

  • HOME
  • Publications
  • Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation

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.