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We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly exceeding those used during training.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Kunho Kim , Sumin Seo , Yongjun Cho , Hyungjin Chung

State-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that the compositional generation power of diffusion models on…

Machine Learning · Computer Science 2025-09-30 Dongmin Park , Sebin Kim , Taehong Moon , Minkyu Kim , Kangwook Lee , Jaewoong Cho

Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to…

Human-Computer Interaction · Computer Science 2024-07-18 Ye Wang , Nicole B. Damen , Thomas Gale , Voho Seo , Hooman Shayani

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

Recently, pre-trained text-to-image (T2I) models have been extensively adopted for real-world image restoration because of their powerful generative prior. However, controlling these large models for image restoration usually requires a…

Computer Vision and Pattern Recognition · Computer Science 2025-04-22 Junyuan Deng , Xinyi Wu , Yongxing Yang , Congchao Zhu , Song Wang , Zhenyao Wu

Personalized text-to-image (T2I) generation has emerged as a key application for creating user-specific concepts from a few reference images. The core challenge is concept disentanglement: separating the target concept from irrelevant…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Minseo Kim , Minchan Kwon , Dongyeun Lee , Yunho Jeon , Junmo Kim

Text-to-image diffusion models have demonstrated the underlying risk of generating various unwanted content, such as sexual elements. To address this issue, the task of concept erasure has been introduced, aiming to erase any undesired…

Computer Vision and Pattern Recognition · Computer Science 2025-06-04 Zheling Meng , Bo Peng , Xiaochuan Jin , Yueming Lyu , Wei Wang , Jing Dong , Tieniu Tan

Modeling and producing lifelike clothed human images has attracted researchers' attention from different areas for decades, with the complexity from highly articulated and structured content. Rendering algorithms decompose and simulate the…

Computer Vision and Pattern Recognition · Computer Science 2024-10-21 Rui Hu , Qian He , Gaofeng He , Jiedong Zhuang , Huang Chen , Huafeng Liu , Huamin Wang

We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework. We develop a highly efficient…

Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Chengyou Jia , Changliang Xia , Zhuohang Dang , Weijia Wu , Hangwei Qian , Minnan Luo

Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Ye Tian , Ling Yang , Haotian Yang , Yuan Gao , Yufan Deng , Jingmin Chen , Xintao Wang , Zhaochen Yu , Xin Tao , Pengfei Wan , Di Zhang , Bin Cui

Text-to-image (T2I) generation has achieved remarkable progress in instruction following and aesthetics. However, a persistent challenge is the prevalence of physical artifacts, such as anatomical and structural flaws, which severely…

Computer Vision and Pattern Recognition · Computer Science 2025-09-15 Jia Wang , Jie Hu , Xiaoqi Ma , Hanghang Ma , Yanbing Zeng , Xiaoming Wei

Existing text-to-image (T2I) diffusion models usually struggle in interpreting complex prompts, especially those with quantity, object-attribute binding, and multi-subject descriptions. In this work, we introduce a semantic panel as the…

Computer Vision and Pattern Recognition · Computer Science 2024-04-10 Yutong Feng , Biao Gong , Di Chen , Yujun Shen , Yu Liu , Jingren Zhou

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Levon Khachatryan , Andranik Movsisyan , Vahram Tadevosyan , Roberto Henschel , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

Large-scale text-guided image diffusion models have shown astonishing results in text-to-image (T2I) generation. However, applying these models to synthesize textures for 3D geometries remains challenging due to the domain gap between 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Jiawei Lu , Yingpeng Zhang , Zengjun Zhao , He Wang , Kun Zhou , Tianjia Shao

Text-to-image (T2I) diffusion models have achieved remarkable success in generating high-quality images from textual prompts. However, their ability to store vast amounts of knowledge raises concerns in scenarios where selective forgetting…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Gen Li , Yang Xiao , Jie Ji , Kaiyuan Deng , Bo Hui , Linke Guo , Xiaolong Ma

Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the previous works cannot precisely control the background and…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Tianle Li , Max Ku , Cong Wei , Wenhu Chen

Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no longer satisfy users' pressing demands for faithful…

This survey reviews the progress of diffusion models in generating images from text, ~\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts with a brief introduction of how diffusion models work for…

Computer Vision and Pattern Recognition · Computer Science 2024-11-11 Chenshuang Zhang , Chaoning Zhang , Mengchun Zhang , In So Kweon , Junmo Kim

Image-to-Image (I2I) translation is a heated topic in academia, and it also has been applied in real-world industry for tasks like image synthesis, super-resolution, and colorization. However, traditional I2I translation methods train data…

Computer Vision and Pattern Recognition · Computer Science 2020-10-28 Jialu Huang , Jing Liao , Sam Kwong
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