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Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolution (SR). However, most existing T2I diffusion models are…

Computer Vision and Pattern Recognition · Computer Science 2024-11-20 Brian B. Moser , Stanislav Frolov , Tobias C. Nauen , Federico Raue , Andreas Dengel

Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. However, recent studies highlight concerns over the use of unauthorized data in training these models, which may…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Soumil Datta , Shih-Chieh Dai , Leo Yu , Guanhong Tao

Generative models, e.g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts. Yet, the generation of 360-degree panorama images from text remains a challenge, particularly due to the dearth of paired…

Computer Vision and Pattern Recognition · Computer Science 2024-04-12 Cheng Zhang , Qianyi Wu , Camilo Cruz Gambardella , Xiaoshui Huang , Dinh Phung , Wanli Ouyang , Jianfei Cai

Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, translating these abilities to fine-grained image editing…

Computer Vision and Pattern Recognition · Computer Science 2024-02-06 Chong Mou , Xintao Wang , Jiechong Song , Ying Shan , Jian Zhang

Recently, the impressive generative capabilities of diffusion models have been demonstrated, producing images with remarkable fidelity. Particularly, existing methods for the 3D object generation tasks, which is one of the fastest-growing…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Jaeseok Lee , Jaekoo Lee

The rapid proliferation of large-scale text-to-image diffusion (T2ID) models has raised serious concerns about their potential misuse in generating harmful content. Although numerous methods have been proposed for erasing undesired concepts…

Computer Vision and Pattern Recognition · Computer Science 2025-04-03 Koushik Srivatsan , Fahad Shamshad , Muzammal Naseer , Vishal M. Patel , Karthik Nandakumar

Diffusion models (DMs) have revolutionized data generation, particularly in text-to-image (T2I) synthesis. However, the widespread use of personalized generative models raises significant concerns regarding privacy violations and copyright…

Computer Vision and Pattern Recognition · Computer Science 2025-02-25 Xinwei Liu , Xiaojun Jia , Yuan Xun , Hua Zhang , Xiaochun Cao

In this paper, we present DesignDiffusion, a simple yet effective framework for the novel task of synthesizing design images from textual descriptions. A primary challenge lies in generating accurate and style-consistent textual and visual…

Computer Vision and Pattern Recognition · Computer Science 2025-03-04 Zhendong Wang , Jianmin Bao , Shuyang Gu , Dong Chen , Wengang Zhou , Houqiang Li

Diffusion models show remarkable image generation performance following text prompts, but risk generating sexual contents. Existing approaches, such as prompt filtering, concept removal, and even sexual contents mitigation methods, struggle…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Jaesin Ahn , Heechul Jung

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

Recently, large-scale text-to-image (T2I) diffusion models have emerged as a powerful tool for image-to-image translation (I2I), allowing open-domain image translation via user-provided text prompts. This paper proposes frequency-controlled…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Xiang Gao , Zhengbo Xu , Junhan Zhao , Jiaying Liu

The remarkable image generation capabilities of state-of-the-art diffusion models, such as Stable Diffusion, can also be misused to spread misinformation and plagiarize copyrighted materials. To mitigate the potential risks associated with…

Computer Vision and Pattern Recognition · Computer Science 2025-06-12 Qiuyu Tang , Bonor Ayambem , Mooi Choo Chuah , Aparna Bharati

In recent years, text generation tools utilizing Artificial Intelligence (AI) have occasionally been misused across various domains, such as generating student reports or creative writings. This issue prompts plagiarism detection services…

Computation and Language · Computer Science 2025-04-14 Ahmed K. Kadhim , Lei Jiao , Rishad Shafik , Ole-Christoffer Granmo

Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and…

Computer Vision and Pattern Recognition · Computer Science 2025-09-10 Omri Avrahami , Or Patashnik , Ohad Fried , Egor Nemchinov , Kfir Aberman , Dani Lischinski , Daniel Cohen-Or

Text-to-image (T2I) diffusion models have drawn attention for their ability to generate high-quality images with precise text alignment. However, these models can also be misused to produce inappropriate content. Existing safety measures,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Hongxiang Zhang , Yifeng He , Hao Chen

We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models. Minority instances, in the context of T2I generation, can be defined as ones living on low-density regions of text-conditional…

Computer Vision and Pattern Recognition · Computer Science 2025-04-07 Soobin Um , Jong Chul Ye

Text-to-image (T2I) generative models such as Stable Diffusion and FLUX can synthesize realistic, high-quality images directly from textual prompts. The resulting image quality depends critically on well-crafted prompts that specify both…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Mingzhe Li , Renhao Zhang , Zhiyang Wen , Siqi Pan , Bruno Castro da Silva , Juan Zhai , Shiqing Ma

Text-to-image generative models such as Stable Diffusion and DALL$\cdot$E raise many ethical concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones. To address these ethical concerns, safety filters are often…

Machine Learning · Computer Science 2023-11-14 Yuchen Yang , Bo Hui , Haolin Yuan , Neil Gong , Yinzhi Cao

Text-to-Image artificial intelligence (AI) recently saw a major breakthrough with the release of Dall-E and its open-source counterpart, Stable Diffusion. These programs allow anyone to create original visual art pieces by simply providing…

Human-Computer Interaction · Computer Science 2023-01-06 Nassim Dehouche , Kullathida Dehouche

Text-to-image (T2I) models such as Stable Diffusion and DALLE remain susceptible to generating harmful or Not-Safe-For-Work (NSFW) content under jailbreak attacks despite deployed safety filters. Existing jailbreak attacks either rely on…

Computer Vision and Pattern Recognition · Computer Science 2026-03-27 Haolun Zheng , Yu He , Tailun Chen , Shuo Shao , Zhixuan Chu , Hongbin Zhou , Lan Tao , Zhan Qin , Kui Ren
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