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Text-to-image generation model is able to generate images across a diverse range of subjects and styles based on a single prompt. Recent works have proposed a variety of interaction methods that help users understand the capabilities of…

Human-Computer Interaction · Computer Science 2023-07-19 Seungho Baek , Hyerin Im , Jiseung Ryu , Juhyeong Park , Takyeon Lee

Text-to-image models have shown remarkable progress in generating high-quality images from user-provided prompts. Despite this, the quality of these images varies due to the models' sensitivity to human language nuances. With advancements…

Artificial Intelligence · Computer Science 2024-06-14 Xinrui Yang , Zhuohan Wang , Anthony Hu

The notable gap between user-provided and model-preferred prompts poses a significant challenge for generating high-quality images with text-to-image models, compelling the need for prompt engineering. Current studies on prompt engineering…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Shiyu Wu , Mingzhen Sun , Weining Wang , Yequan Wang , Jing Liu

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense…

Human-Computer Interaction · Computer Science 2024-03-08 Zhijie Wang , Yuheng Huang , Da Song , Lei Ma , Tianyi Zhang

Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of…

Computation and Language · Computer Science 2023-12-08 Xinyuan Wang , Chenxi Li , Zhen Wang , Fan Bai , Haotian Luo , Jiayou Zhang , Nebojsa Jojic , Eric P. Xing , Zhiting Hu

Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of laborious human engineering, we propose prompt adaptation,…

Computation and Language · Computer Science 2024-01-01 Yaru Hao , Zewen Chi , Li Dong , Furu Wei

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be…

Artificial Intelligence · Computer Science 2023-11-02 Yingchaojie Feng , Xingbo Wang , Kam Kwai Wong , Sijia Wang , Yuhong Lu , Minfeng Zhu , Baicheng Wang , Wei Chen

Text entry is an essential task in our day-to-day digital interactions. Numerous intelligent features have been developed to streamline this process, making text entry more effective, efficient, and fluid. These improvements include…

Computation and Language · Computer Science 2023-10-17 Junxiao Shen , John J. Dudley , Jingyao Zheng , Bill Byrne , Per Ola Kristensson

Recent advancements in text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in generating high-fidelity images. However, these models often struggle to faithfully render complex user prompts, particularly in…

Computer Vision and Pattern Recognition · Computer Science 2025-09-24 Linqing Wang , Ximing Xing , Yiji Cheng , Zhiyuan Zhao , Donghao Li , Tiankai Hang , Jiale Tao , Qixun Wang , Ruihuang Li , Comi Chen , Xin Li , Mingrui Wu , Xinchi Deng , Shuyang Gu , Chunyu Wang , Qinglin Lu

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Siddhartha Datta , Alexander Ku , Deepak Ramachandran , Peter Anderson

Despite impressive recent advances in text-to-image diffusion models, obtaining high-quality images often requires prompt engineering by humans who have developed expertise in using them. In this work, we present NeuroPrompts, an adaptive…

Artificial Intelligence · Computer Science 2024-04-09 Shachar Rosenman , Vasudev Lal , Phillip Howard

Text-to-image generative models have demonstrated remarkable capabilities in generating high-quality images based on textual prompts. However, crafting prompts that accurately capture the user's creative intent remains challenging. It often…

Human-Computer Interaction · Computer Science 2023-04-20 Stephen Brade , Bryan Wang , Mauricio Sousa , Sageev Oore , Tovi Grossman

Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language…

Computer Vision and Pattern Recognition · Computer Science 2025-05-26 Mingrui Wu , Lu Wang , Pu Zhao , Fangkai Yang , Jianjin Zhang , Jianfeng Liu , Yuefeng Zhan , Weihao Han , Hao Sun , Jiayi Ji , Xiaoshuai Sun , Qingwei Lin , Weiwei Deng , Dongmei Zhang , Feng Sun , Qi Zhang , Rongrong Ji

Prompt optimization is essential for effective utilization of large language models (LLMs) across diverse tasks. While existing optimization methods are effective in optimizing short prompts, they struggle with longer, more complex ones,…

Computation and Language · Computer Science 2025-07-18 Shanu Kumar , Akhila Yesantarao Venkata , Shubhanshu Khandelwal , Bishal Santra , Parag Agrawal , Manish Gupta

Generative AI models have shown impressive ability to produce images with text prompts, which could benefit creativity in visual art creation and self-expression. However, it is unclear how precisely the generated images express contexts…

Human-Computer Interaction · Computer Science 2023-03-21 Yunlong Wang , Shuyuan Shen , Brian Y. Lim

Text-to-image generative models often struggle with long prompts detailing complex scenes, diverse objects with distinct visual characteristics and spatial relationships. In this work, we propose SCoPE (Scheduled interpolation of…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Ketan Suhaas Saichandran , Xavier Thomas , Prakhar Kaushik , Deepti Ghadiyaram

Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic images. Despite the progress, these models still struggle to…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Oscar Mañas , Pietro Astolfi , Melissa Hall , Candace Ross , Jack Urbanek , Adina Williams , Aishwarya Agrawal , Adriana Romero-Soriano , Michal Drozdzal

Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications, and continue to fascinate researchers across fields for…

Computation and Language · Computer Science 2025-11-10 Deepak Pandita , Tharindu Cyril Weerasooriya , Ankit Parag Shah , Isabelle Diana May-Xin Ng , Christopher M. Homan , Wei Wei

Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often require multiple passes of prompt engineering by humans in order…

Computation and Language · Computer Science 2023-11-14 Tingfeng Cao , Chengyu Wang , Bingyan Liu , Ziheng Wu , Jinhui Zhu , Jun Huang

Current text-to-image (T2I) benchmarks evaluate models on rigid prompts, potentially underestimating true generative capabilities due to prompt sensitivity and creating biases that favor certain models while disadvantaging others. We…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Haosheng Gan , Berk Tinaz , Mohammad Shahab Sepehri , Zalan Fabian , Mahdi Soltanolkotabi
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