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The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work…

Computer Vision and Pattern Recognition · Computer Science 2025-04-22 Brian Nlong Zhao , Yuhang Xiao , Jiashu Xu , Xinyang Jiang , Yifan Yang , Dongsheng Li , Laurent Itti , Vibhav Vineet , Yunhao Ge

Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development of algorithms for automated prompt generation. However, these…

Computer Vision and Pattern Recognition · Computer Science 2025-08-19 Yutong He , Alexander Robey , Naoki Murata , Yiding Jiang , Joshua Nathaniel Williams , George J. Pappas , Hamed Hassani , Yuki Mitsufuji , Ruslan Salakhutdinov , J. Zico Kolter

Personalized diffusion models have shown remarkable success in Text-to-Image (T2I) generation by enabling the injection of user-defined concepts into diverse contexts. However, balancing concept fidelity with contextual alignment remains a…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Shamil Ayupov , Maksim Nakhodnov , Anastasia Yaschenko , Andrey Kuznetsov , Aibek Alanov

Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However, many generated images still suffer from issues such as…

The rapid progress in diffusion-based text-to-image (T2I) generation has created an urgent need for interpretable automatic evaluation methods that can assess the quality of generated images, therefore reducing the human annotation burden.…

Artificial Intelligence · Computer Science 2025-05-26 Zi-Ao Ma , Tian Lan , Rong-Cheng Tu , Shu-Hang Liu , Heyan Huang , Zhijing Wu , Chen Xu , Xian-Ling Mao

This work investigates a challenging task named open-domain interleaved image-text generation, which generates interleaved texts and images following an input query. We propose a new interleaved generation framework based on prompting…

Computer Vision and Pattern Recognition · Computer Science 2023-11-07 Jie An , Zhengyuan Yang , Linjie Li , Jianfeng Wang , Kevin Lin , Zicheng Liu , Lijuan Wang , Jiebo Luo

Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos. However, achieving the desired output often…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Yatai Ji , Jiacheng Zhang , Jie Wu , Shilong Zhang , Shoufa Chen , Chongjian GE , Peize Sun , Weifeng Chen , Wenqi Shao , Xuefeng Xiao , Weilin Huang , Ping Luo

In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guided generative functions. However, relying solely on text for…

Computer Vision and Pattern Recognition · Computer Science 2026-01-09 Pu Cao , Feng Zhou , Qing Song , Lu Yang

Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with high photorealism starting from a given descriptive text as…

Computers and Society · Computer Science 2023-04-14 Ranjita Naik , Besmira Nushi

Text-to-image (T2I) diffusion models such as SDXL and FLUX have achieved impressive photorealism, yet small-scale distortions remain pervasive in limbs, face, text and so on. Existing refinement approaches either perform costly iterative…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Shaocheng Shen , Jianfeng Liang , Chunlei Cai , Cong Geng , Huiyu Duan , Xiaoyun Zhang , Qiang Hu , Guangtao Zhai

Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs more usable, aligning them with human preferences is essential.…

Computation and Language · Computer Science 2024-10-21 Mozhi Zhang , Pengyu Wang , Chenkun Tan , Mianqiu Huang , Dong Zhang , Yaqian Zhou , Xipeng Qiu

Text-to-image (T2I) models have recently experienced rapid development, achieving astonishing performance in terms of fidelity and textual alignment capabilities. However, given a long paragraph (up to 512 words), these generation models…

Computer Vision and Pattern Recognition · Computer Science 2025-05-07 Weijia Wu , Zhuang Li , Yefei He , Mike Zheng Shou , Chunhua Shen , Lele Cheng , Yan Li , Tingting Gao , Di Zhang

Diffusion-driven text-to-image (T2I) generation has achieved remarkable advancements in recent years. To further improve T2I models' capability in numerical and spatial reasoning, layout is employed as an intermedium to bridge large…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Yuhao Jia , Wenhan Tan

Recent spatial control methods for text-to-image (T2I) diffusion models have shown compelling results. However, these methods still fail to precisely follow the control conditions and generate the corresponding images, especially when…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Jiaze Wang , Rui Chen , Haowang Cui

The issue of generative pretraining for vision models has persisted as a long-standing conundrum. At present, the text-to-image (T2I) diffusion model demonstrates remarkable proficiency in generating high-definition images matching textual…

Computer Vision and Pattern Recognition · Computer Science 2023-12-25 Qiang Wan , Zilong Huang , Bingyi Kang , Jiashi Feng , Li Zhang

TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Shih-Ying Yeh , Yi Li , Sang-Hyun Park , Giyeong Oh , Xuehai Wang , Min Song , Youngjae Yu , Shang-Hong Lai

Text-to-Image (T2I) diffusion/flow models have recently achieved remarkable progress in visual fidelity and text alignment. However, they remain limited when users need to precisely control image layouts, something that natural language…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Amadou S. Sangare , Adrien Maglo , Mohamed Chaouch , Bertrand Luvison

Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Minglei Shi , Haolin Wang , Borui Zhang , Wenzhao Zheng , Bohan Zeng , Ziyang Yuan , Xiaoshi Wu , Yuanxing Zhang , Huan Yang , Xintao Wang , Pengfei Wan , Kun Gai , Jie Zhou , Jiwen Lu

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the…

Computer Vision and Pattern Recognition · Computer Science 2023-03-21 Chong Mou , Xintao Wang , Liangbin Xie , Yanze Wu , Jian Zhang , Zhongang Qi , Ying Shan , Xiaohu Qie

Despite their impressive capabilities, diffusion-based text-to-image (T2I) models can lack faithfulness to the text prompt, where generated images may not contain all the mentioned objects, attributes or relations. To alleviate these…

Computer Vision and Pattern Recognition · Computer Science 2023-05-23 Shyamgopal Karthik , Karsten Roth , Massimiliano Mancini , Zeynep Akata