@inproceedings{cai-etal-2025-bayesian,
title = "{B}ayesian Optimization for Controlled Image Editing via {LLM}s",
author = "Cai, Chengkun and
Liu, Haoliang and
Zhao, Xu and
Jiang, Zhongyu and
Zhang, Tianfang and
Wu, Zongkai and
Lee, John and
Hwang, Jenq-Neng and
Li, Lei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.523/",
doi = "10.18653/v1/2025.findings-acl.523",
pages = "10045--10056",
ISBN = "979-8-89176-256-5",
abstract = "In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. To address these challenges, we propose BayesGenie, an off-the-shelf approach that integrates Large Language Models (LLMs) with Bayesian Optimization to facilitate precise and user-friendly image editing. Our method enables users to modify images through natural language descriptions without manual area marking, while preserving the original image{'}s semantic integrity. Unlike existing techniques that require extensive pre-training or fine-tuning, our approach demonstrates remarkable adaptability across various LLMs through its model-agnostic design. BayesGenie employs an adapted Bayesian optimization strategy to automatically refine the inference process parameters, achieving high-precision image editing with minimal user intervention. Through extensive experiments across diverse scenarios, we demonstrate that our framework outperforms existing methods in both editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4."
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<abstract>In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. To address these challenges, we propose BayesGenie, an off-the-shelf approach that integrates Large Language Models (LLMs) with Bayesian Optimization to facilitate precise and user-friendly image editing. Our method enables users to modify images through natural language descriptions without manual area marking, while preserving the original image’s semantic integrity. Unlike existing techniques that require extensive pre-training or fine-tuning, our approach demonstrates remarkable adaptability across various LLMs through its model-agnostic design. BayesGenie employs an adapted Bayesian optimization strategy to automatically refine the inference process parameters, achieving high-precision image editing with minimal user intervention. Through extensive experiments across diverse scenarios, we demonstrate that our framework outperforms existing methods in both editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4.</abstract>
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%0 Conference Proceedings
%T Bayesian Optimization for Controlled Image Editing via LLMs
%A Cai, Chengkun
%A Liu, Haoliang
%A Zhao, Xu
%A Jiang, Zhongyu
%A Zhang, Tianfang
%A Wu, Zongkai
%A Lee, John
%A Hwang, Jenq-Neng
%A Li, Lei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F cai-etal-2025-bayesian
%X In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. To address these challenges, we propose BayesGenie, an off-the-shelf approach that integrates Large Language Models (LLMs) with Bayesian Optimization to facilitate precise and user-friendly image editing. Our method enables users to modify images through natural language descriptions without manual area marking, while preserving the original image’s semantic integrity. Unlike existing techniques that require extensive pre-training or fine-tuning, our approach demonstrates remarkable adaptability across various LLMs through its model-agnostic design. BayesGenie employs an adapted Bayesian optimization strategy to automatically refine the inference process parameters, achieving high-precision image editing with minimal user intervention. Through extensive experiments across diverse scenarios, we demonstrate that our framework outperforms existing methods in both editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4.
%R 10.18653/v1/2025.findings-acl.523
%U https://aclanthology.org/2025.findings-acl.523/
%U https://doi.org/10.18653/v1/2025.findings-acl.523
%P 10045-10056
Markdown (Informal)
[Bayesian Optimization for Controlled Image Editing via LLMs](https://aclanthology.org/2025.findings-acl.523/) (Cai et al., Findings 2025)
ACL
- Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, and Lei Li. 2025. Bayesian Optimization for Controlled Image Editing via LLMs. In Findings of the Association for Computational Linguistics: ACL 2025, pages 10045–10056, Vienna, Austria. Association for Computational Linguistics.