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Diffusion-based text-to-image generative models, e.g., Stable Diffusion, have revolutionized the field of content generation, enabling significant advancements in areas like image editing and video synthesis. Despite their formidable…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Yanyu Li , Xian Liu , Anil Kag , Ju Hu , Yerlan Idelbayev , Dhritiman Sagar , Yanzhi Wang , Sergey Tulyakov , Jian Ren

Recently, text-to-3D approaches have achieved high-fidelity 3D content generation using text description. However, the generated objects are stochastic and lack fine-grained control. Sketches provide a cheap approach to introduce such…

Computer Vision and Pattern Recognition · Computer Science 2024-01-30 Minglin Chen , Weihao Yuan , Yukun Wang , Zhe Sheng , Yisheng He , Zilong Dong , Liefeng Bo , Yulan Guo

Large-scale generative models, such as text-to-image diffusion models, have garnered widespread attention across diverse domains due to their creative and high-fidelity image generation. Nonetheless, existing large-scale diffusion models…

Computer Vision and Pattern Recognition · Computer Science 2024-08-28 Younghyun Kim , Geunmin Hwang , Junyu Zhang , Eunbyung Park

Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency. However, such fine-tuned models are not robust; they often fail to compose with concepts of pretrained…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Kyungmin Lee , Sangkyung Kwak , Kihyuk Sohn , Jinwoo Shin

Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating high-fidelity visuals, text-to-image models can still struggle…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Taewook Kim , Ze Wang , Zhengyuan Yang , Jiang Wang , Lijuan Wang , Zicheng Liu , Qiang Qiu

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

Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts. Among various approaches, diffusion models have achieved…

Computer Vision and Pattern Recognition · Computer Science 2026-01-26 Xuyang Guo , Jiayan Huo , Yingyu Liang , Zhenmei Shi , Zhao Song , Jiahao Zhang , Zhen Zhuang

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

With the advancements in denoising diffusion probabilistic models (DDPMs), image inpainting has significantly evolved from merely filling information based on nearby regions to generating content conditioned on various prompts such as text,…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Lingzhi Pan , Tong Zhang , Bingyuan Chen , Qi Zhou , Wei Ke , Sabine Süsstrunk , Mathieu Salzmann

The rapid progress in diffusion models, transformers, and language agents has unlocked new possibilities, yet their potential in user interfaces and commercial applications remains underexplored. We present Sketch-Search Agent, a novel…

Information Retrieval · Computer Science 2025-04-15 Edward Sun

Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Text-to-image generation using neural networks could be traced back to the emergence of…

Computer Vision and Pattern Recognition · Computer Science 2023-09-06 Fengxiang Bie , Yibo Yang , Zhongzhu Zhou , Adam Ghanem , Minjia Zhang , Zhewei Yao , Xiaoxia Wu , Connor Holmes , Pareesa Golnari , David A. Clifton , Yuxiong He , Dacheng Tao , Shuaiwen Leon Song

Diffusion models have recently demonstrated their effectiveness in generating extremely high-quality images and are now utilized in a wide range of applications, including automatic sketch colorization. Although many methods have been…

Computer Vision and Pattern Recognition · Computer Science 2024-07-04 Dingkun Yan , Liang Yuan , Erwin Wu , Yuma Nishioka , Issei Fujishiro , Suguru Saito

Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these advances, existing models struggle with complex prompts…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Eric Hanchen Jiang , Yasi Zhang , Zhi Zhang , Yixin Wan , Andrew Lizarraga , Shufan Li , Ying Nian Wu

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

Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is…

Computer Vision and Pattern Recognition · Computer Science 2024-02-08 Jinming Su , Songen Gu , Yiting Duan , Xingyue Chen , Junfeng Luo

Diffusion models generate images with an unprecedented level of quality, but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spatially disentangled latent codes, but these methods do not…

Computer Vision and Pattern Recognition · Computer Science 2024-04-11 Jiawei Ren , Mengmeng Xu , Jui-Chieh Wu , Ziwei Liu , Tao Xiang , Antoine Toisoul

This paper does not describe a new method; instead, it provides a thorough exploration of an important yet understudied design space related to recent advances in text-to-image synthesis -- specifically, the deep fusion of large language…

Computer Vision and Pattern Recognition · Computer Science 2025-05-16 Bingda Tang , Boyang Zheng , Xichen Pan , Sayak Paul , Saining Xie

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Pengzhi Li , QInxuan Huang , Yikang Ding , Zhiheng Li

While text-to-image diffusion models can generate highquality images from textual descriptions, they generally lack fine-grained control over the visual composition of the generated images. Some recent works tackle this problem by training…

Computer Vision and Pattern Recognition · Computer Science 2024-02-22 Denis Lukovnikov , Asja Fischer

Novel diffusion models can synthesize photo-realistic images with integrated high-quality text. Surprisingly, we demonstrate through attention activation patching that only less than $1$% of diffusion models' parameters, all contained in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Łukasz Staniszewski , Bartosz Cywiński , Franziska Boenisch , Kamil Deja , Adam Dziedzic