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Related papers: Prompt-Free Diffusion: Taking "Text" out of Text-t…

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With the open-sourcing of text-to-image models (T2I) such as stable diffusion (SD) and stable diffusion XL (SD-XL), there is an influx of models fine-tuned in specific domains based on the open-source SD model, such as in anime, character…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Ruyi Gan , Xiaojun Wu , Junyu Lu , Yuanhe Tian , Dixiang Zhang , Ziwei Wu , Renliang Sun , Chang Liu , Jiaxing Zhang , Pingjian Zhang , Yan Song

Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can generate offensive or disturbing imagery when provided with harmful inputs. Existing…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Chi Zhang , Changjia Zhu , Xiaowen Li , Yao Liu , Zhuo Lu

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

Text-to-image diffusion models enable high-quality image generation but are computationally expensive. While prior work optimizes per-inference efficiency, we explore an orthogonal approach: reducing redundancy across correlated prompts.…

Computer Vision and Pattern Recognition · Computer Science 2025-08-29 Dale Decatur , Thibault Groueix , Wang Yifan , Rana Hanocka , Vladimir Kim , Matheus Gadelha

While modern text-to-image (T2I) models excel at generating images from intricate prompts, they struggle to capture the key details when the inputs are descriptive paragraphs. This limitation stems from the prevalence of concise captions…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Jen-Yuan Huang , Tong Lin , Yilun Du

Visual prompt, a pair of before-and-after edited images, can convey indescribable imagery transformations and prosper in image editing. However, current visual prompt methods rely on a pretrained text-guided image-to-image generative model…

Computer Vision and Pattern Recognition · Computer Science 2025-01-28 Pengcheng Xu , Qingnan Fan , Fei Kou , Shuai Qin , Hong Gu , Ruoyu Zhao , Charles Ling , Boyu Wang

Text-to-Image (T2I) models have made remarkable progress in generating images from text prompts, but their output quality and safety still depend heavily on how prompts are phrased. Existing safety methods typically refine prompts using…

Computer Vision and Pattern Recognition · Computer Science 2025-09-18 Jinwoo Jeon , JunHyeok Oh , Hayeong Lee , Byung-Jun Lee

Image composition has advanced significantly with large-scale pre-trained T2I diffusion models. Despite progress in same-domain composition, cross-domain composition remains under-explored. The main challenges are the stochastic nature of…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Haowen Li , Zhenfeng Fan , Zhang Wen , Zhengzhou Zhu , Yunjin Li

Text-to-Image (T2I) generative models have revolutionized content creation but remain highly sensitive to prompt phrasing, often requiring users to repeatedly refine prompts multiple times without clear feedback. While techniques such as…

Computer Vision and Pattern Recognition · Computer Science 2025-07-30 Chieh-Yun Chen , Min Shi , Gong Zhang , Humphrey Shi

Diffusion models excel in many generative modeling tasks, notably in creating images from text prompts, a task referred to as text-to-image (T2I) generation. Despite the ability to generate high-quality images, these models often replicate…

Multimedia · Computer Science 2024-02-20 Yang Zhang , Teoh Tze Tzun , Lim Wei Hern , Haonan Wang , Kenji Kawaguchi

Recent advances in text-to-image diffusion models have enabled the photorealistic generation of images from text prompts. Despite the great progress, existing models still struggle to generate compositional multi-concept images naturally,…

Computer Vision and Pattern Recognition · Computer Science 2023-10-12 Hazarapet Tunanyan , Dejia Xu , Shant Navasardyan , Zhangyang Wang , Humphrey Shi

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

The rapid advancement of Text-to-Image(T2I) generative models has enabled the synthesis of high-quality images guided by textual descriptions. Despite this significant progress, these models are often susceptible in generating contents that…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Yichen Sun , Zhixuan Chu , Zhan Qin , Kui Ren

While diffusion-based text-to-image (T2I) models provide a simple and powerful way to generate images, guiding this generation remains a challenge. For concepts that are difficult to describe through language, users may struggle to create…

Human-Computer Interaction · Computer Science 2023-08-11 John Joon Young Chung , Eytan Adar

Text-guided image manipulation with diffusion models enables flexible and precise editing based on prompts, but raises ethical and copyright concerns due to potential unauthorized modifications. To address this, we propose SecureT2I, a…

Cryptography and Security · Computer Science 2025-07-08 Xiaodong Wu , Xiangman Li , Qi Li , Jianbing Ni , Rongxing Lu

Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. However, these methods are constrained by the need for manual…

Computer Vision and Pattern Recognition · Computer Science 2025-05-21 Yuanyuan Chang , Yinghua Yao , Tao Qin , Mengmeng Wang , Ivor Tsang , Guang Dai

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

The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models. Within just two years of development, it was unprecedentedly of high-quality, diversity, and…

Artificial Intelligence · Computer Science 2023-10-16 Zeqiang Lai , Xizhou Zhu , Jifeng Dai , Yu Qiao , Wenhai Wang

Recent advancements in text-to-image diffusion models have yielded impressive results in generating realistic and diverse images. However, these models still struggle with complex prompts, such as those that involve numeracy and spatial…

Computer Vision and Pattern Recognition · Computer Science 2024-03-05 Long Lian , Boyi Li , Adam Yala , Trevor Darrell

Recent text-driven image editing in diffusion models has shown remarkable success. However, the existing methods assume that the user's description sufficiently grounds the contexts in the source image, such as objects, background, style,…

Computer Vision and Pattern Recognition · Computer Science 2023-06-06 Sunwoo Kim , Wooseok Jang , Hyunsu Kim , Junho Kim , Yunjey Choi , Seungryong Kim , Gayeong Lee