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Recently, the strong latent Diffusion Probabilistic Model (DPM) has been applied to high-quality Text-to-Image (T2I) generation (e.g., Stable Diffusion), by injecting the encoded target text prompt into the gradually denoised diffusion…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Mingyang Yi , Aoxue Li , Yi Xin , Zhenguo Li

Large-scale Text-to-Image (T2I) diffusion models demonstrate significant generation capabilities based on textual prompts. Based on the T2I diffusion models, text-guided image editing research aims to empower users to manipulate generated…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Chuanming Tang , Kai Wang , Fei Yang , Joost van de Weijer

Current large-scale generative models have impressive efficiency in generating high-quality images based on text prompts. However, they lack the ability to precisely control the size and position of objects in the generated image. In this…

Computer Vision and Pattern Recognition · Computer Science 2023-04-27 Jiafeng Mao , Xueting Wang

Text-to-image (T2I) generation aims to synthesize images from textual prompts, which jointly specify what must be shown and imply what can be inferred, which thus correspond to two core capabilities: \textbf{\textit{composition}} and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Ouxiang Li , Yuan Wang , Xinting Hu , Huijuan Huang , Rui Chen , Jiarong Ou , Xin Tao , Pengfei Wan , Xiaojuan Qi , Fuli Feng

Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Ashish Goswami , Satyam Kumar Modi , Santhosh Rishi Deshineni , Harman Singh , Prathosh A. P , Parag Singla

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) diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of these models can inadvertently led they to…

Computer Vision and Pattern Recognition · Computer Science 2025-02-19 Die Chen , Zhiwen Li , Cen Chen , Xiaodan Li , Jinyan Ye

Diffusion models are the state of the art in text-to-image generation, but their perceptual variability remains understudied. In this paper, we examine how prompts affect image variability in black-box diffusion-based models. We propose…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Raphael Tang , Xinyu Zhang , Lixinyu Xu , Yao Lu , Wenyan Li , Pontus Stenetorp , Jimmy Lin , Ferhan Ture

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Michal Geyer , Omer Bar-Tal , Shai Bagon , Tali Dekel

With the advance of generative AI, the text-to-image (T2I) model has the ability to generate various contents. However, the generated contents cannot be fully controlled. There is a potential risk that T2I model can generate unsafe images…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Dong Han , Salaheldin Mohamed , Yong Li

Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional…

Machine Learning · Computer Science 2025-07-22 Yi Zhang , Zhen Chen , Chih-Hong Cheng , Wenjie Ruan , Xiaowei Huang , Dezong Zhao , David Flynn , Siddartha Khastgir , Xingyu Zhao

Numerosity remains a challenge for state-of-the-art text-to-image generation models like FLUX and GPT-4o, which often fail to accurately follow counting instructions in text prompts. In this paper, we aim to study a fundamental yet often…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Yaqi Zhao , Xiaochen Wang , Li Dong , Wentao Zhang , Yuhui Yuan

Despite the impressive text-to-image (T2I) synthesis capabilities of diffusion models, they often struggle to understand compositional relationships between objects and attributes, especially in complex settings. Existing solutions have…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Evans Xu Han , Linghao Jin , Xiaofeng Liu , Paul Pu Liang

Text-to-image generation models can create high-quality images from input prompts. However, they struggle to support the consistent generation of identity-preserving requirements for storytelling. Existing approaches to this problem…

Computer Vision and Pattern Recognition · Computer Science 2025-02-06 Tao Liu , Kai Wang , Senmao Li , Joost van de Weijer , Fahad Shahbaz Khan , Shiqi Yang , Yaxing Wang , Jian Yang , Ming-Ming Cheng

Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for…

Computer Vision and Pattern Recognition · Computer Science 2024-04-04 Hao Li , Yang Zou , Ying Wang , Orchid Majumder , Yusheng Xie , R. Manmatha , Ashwin Swaminathan , Zhuowen Tu , Stefano Ermon , Stefano Soatto

Generating images with embedded text is crucial for the automatic production of visual and multimodal documents, such as educational materials and advertisements. However, existing diffusion-based text-to-image models often struggle to…

Computer Vision and Pattern Recognition · Computer Science 2025-03-19 Forouzan Fallah , Maitreya Patel , Agneet Chatterjee , Vlad I. Morariu , Chitta Baral , Yezhou Yang

Text-to-image (TTI) generative models can be used to generate photorealistic images from a given text-string input. These models offer great potential to mitigate challenges to the uptake of machine learning in the earth sciences. However,…

Computer Vision and Pattern Recognition · Computer Science 2023-12-14 C Kupferschmidt , A. D. Binns , K. L. Kupferschmidt , G. W Taylor

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…

Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that grasping the cross-modality alignment is not a necessity for a…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Shuailei Ma , Kecheng Zheng , Ying Wei , Wei Wu , Fan Lu , Yifei Zhang , Chen-Wei Xie , Biao Gong , Jiapeng Zhu , Yujun Shen

Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have achieved unprecedented quality of photorealism with state-of-the-art FID scores on MS-COCO and other generation benchmarks. Given a caption, image generation requires…

Computer Vision and Pattern Recognition · Computer Science 2023-10-24 Samyadeep Basu , Nanxuan Zhao , Vlad Morariu , Soheil Feizi , Varun Manjunatha
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