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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

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

Recent training-free layout-to-image diffusion models have demonstrated remarkable performance in generating high-quality images with controllable layouts. These models follow a one-stage framework: Encouraging the model to focus the…

Computer Vision and Pattern Recognition · Computer Science 2025-03-04 Linhao Huang , Jing Yu

Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image region. To address this, we propose DenseDiffusion, a…

Computer Vision and Pattern Recognition · Computer Science 2023-08-25 Yunji Kim , Jiyoung Lee , Jin-Hwa Kim , Jung-Woo Ha , Jun-Yan Zhu

Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-image diffusion models still have several issues, including…

Computer Vision and Pattern Recognition · Computer Science 2024-03-07 Takahiro Shirakawa , Seiichi Uchida

Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object…

Computer Vision and Pattern Recognition · Computer Science 2019-10-16 Bo Zhao , Lili Meng , Weidong Yin , Leonid Sigal

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

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

The Stable Diffusion Model (SDM) is a prevalent and effective model for text-to-image (T2I) and image-to-image (I2I) generation. Despite various attempts at sampler optimization, model distillation, and network quantification, these…

Computer Vision and Pattern Recognition · Computer Science 2024-06-18 Jinchao Zhu , Yuxuan Wang , Siyuan Pan , Pengfei Wan , Di Zhang , Gao Huang

Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in…

Computer Vision and Pattern Recognition · Computer Science 2024-02-27 Hanan Gani , Shariq Farooq Bhat , Muzammal Naseer , Salman Khan , Peter Wonka

Text-to-image (T2I) generation model has made significant advancements, resulting in high-quality images aligned with an input prompt. However, despite T2I generation's ability to generate fine-grained images, it still faces challenges in…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Taekyung Lee , Donggyu Lee , Myungjoo Kang

Despite their generative power, diffusion models struggle to maintain style consistency across images conditioned on the same style prompt, hindering their practical deployment in creative workflows. While several training-free methods…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Jiexuan Zhang , Yiheng Du , Qian Wang , Weiqi Li , Yu Gu , Jian Zhang

We present a novel Transformer-based network architecture for instance-aware image-to-image translation, dubbed InstaFormer, to effectively integrate global- and instance-level information. By considering extracted content features from an…

Computer Vision and Pattern Recognition · Computer Science 2022-03-31 Soohyun Kim , Jongbeom Baek , Jihye Park , Gyeongnyeon Kim , Seungryong Kim

Text-to-Image (T2I) models have recently achieved remarkable success in generating images from textual descriptions. However, challenges still persist in accurately rendering complex scenes where actions and interactions form the primary…

Computer Vision and Pattern Recognition · Computer Science 2025-09-22 Vatsal Malaviya , Agneet Chatterjee , Maitreya Patel , Yezhou Yang , Chitta Baral

State-of-the-art text-to-image models produce visually impressive results but often struggle with precise alignment to text prompts, leading to missing critical elements or unintended blending of distinct concepts. We propose a novel…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Paul Grimal , Michaël Soumm , Hervé Le Borgne , Olivier Ferret , Akihiro Sugimoto

Text-to-image (T2I) generation has made remarkable progress in producing high-quality images, but a fundamental challenge remains: creating backgrounds that naturally accommodate text placement without compromising image quality. This…

Computer Vision and Pattern Recognition · Computer Science 2025-05-14 Tianyi Liang , Jiangqi Liu , Yifei Huang , Shiqi Jiang , Jianshen Shi , Changbo Wang , Chenhui Li

While text-to-image models have achieved impressive capabilities in image generation and editing, their application across various modalities often necessitates training separate models. Inspired by existing method of single image editing…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Gihyun Kwon , Jangho Park , Jong Chul Ye

Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures are typically limited to text-based filtering or concept…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Runtao Liu , I Chieh Chen , Jindong Gu , Jipeng Zhang , Renjie Pi , Qifeng Chen , Philip Torr , Ashkan Khakzar , Fabio Pizzati

Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I) generation has been proposed to leverage region-specific…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Hui Zhang , Dexiang Hong , Yitong Wang , Jie Shao , Xinglong Wu , Zuxuan Wu , Yu-Gang Jiang

Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout-to-Image (L2I)…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Xianbao Hou , Yonghao He , Zeyd Boukhers , John See , Hu Su , Wei Sui , Cong Yang