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Related papers: DC-ControlNet: Decoupling Inter- and Intra-Element…

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We provide a two-way integration for the widely adopted ControlNet by integrating external condition generation algorithms into a single dense prediction method and incorporating its individually trained image generation processes into a…

Computer Vision and Pattern Recognition · Computer Science 2024-06-11 Yilin Wang , Haiyang Xu , Xiang Zhang , Zeyuan Chen , Zhizhou Sha , Zirui Wang , Zhuowen Tu

User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation…

Computer Vision and Pattern Recognition · Computer Science 2021-03-24 Hongyu Liu , Ziyu Wan , Wei Huang , Yibing Song , Xintong Han , Jing Liao , Bing Jiang , Wei Liu

To enhance the controllability of text-to-image diffusion models, current ControlNet-like models have explored various control signals to dictate image attributes. However, existing methods either handle conditions inefficiently or use a…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Qingdong He , Jinlong Peng , Pengcheng Xu , Boyuan Jiang , Xiaobin Hu , Donghao Luo , Yong Liu , Yabiao Wang , Chengjie Wang , Xiangtai Li , Jiangning Zhang

Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. However, relying solely on text guidance for video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Cong Wang , Jiaxi Gu , Panwen Hu , Haoyu Zhao , Yuanfan Guo , Jianhua Han , Hang Xu , Xiaodan Liang

Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions. However, despite their success, text descriptions often…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Shihao Zhao , Dongdong Chen , Yen-Chun Chen , Jianmin Bao , Shaozhe Hao , Lu Yuan , Kwan-Yee K. Wong

Diffusion models have advanced from text-to-image (T2I) to image-to-image (I2I) generation by incorporating structured inputs such as depth maps, enabling fine-grained spatial control. However, existing methods either train separate models…

Computer Vision and Pattern Recognition · Computer Science 2025-08-01 Yucheng Xie , Fu Feng , Ruixiao Shi , Jing Wang , Yong Rui , Xin Geng

Image composition targets at synthesizing a realistic composite image from a pair of foreground and background images. Recently, generative composition methods are built on large pretrained diffusion models to generate composite images,…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Bo Zhang , Yuxuan Duan , Jun Lan , Yan Hong , Huijia Zhu , Weiqiang Wang , Li Niu

Complex scenario of ultrasound image, in which adjacent tissues (i.e., background) share similar intensity with and even contain richer texture patterns than lesion region (i.e., foreground), brings a unique challenge for accurate lesion…

Image and Video Processing · Electrical Eng. & Systems 2023-08-03 Zhenyuan Ning , Yixiao Mao , Qianjin Feng , Shengzhou Zhong , Yu Zhang

Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Can Qin , Shu Zhang , Ning Yu , Yihao Feng , Xinyi Yang , Yingbo Zhou , Huan Wang , Juan Carlos Niebles , Caiming Xiong , Silvio Savarese , Stefano Ermon , Yun Fu , Ran Xu

The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings. Recently, different methods have been proposed to learn object-centric…

Computer Vision and Pattern Recognition · Computer Science 2022-01-11 Angel Villar-Corrales , Sven Behnke

Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle with compositional visual conditioning - simultaneously…

Computer Vision and Pattern Recognition · Computer Science 2026-05-25 Yanjie Pan , Qingdong He , Zhengkai Jiang , Pengcheng Xu , Chaoyi Wang , Jinlong Peng , Haoxuan Wang , Yun Cao , Zhenye Gan , Mingmin Chi , Bo Peng , Yabiao Wang

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

We present LooseControl to allow generalized depth conditioning for diffusion-based image generation. ControlNet, the SOTA for depth-conditioned image generation, produces remarkable results but relies on having access to detailed depth…

Computer Vision and Pattern Recognition · Computer Science 2023-12-07 Shariq Farooq Bhat , Niloy J. Mitra , Peter Wonka

Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. ControlNet addresses this by introducing an auxiliary conditioning module, while ControlNet++…

Computer Vision and Pattern Recognition · Computer Science 2025-07-04 Nina Konovalova , Maxim Nikolaev , Andrey Kuznetsov , Aibek Alanov

This work presents Controllable Layer Decomposition (CLD), a method for achieving fine-grained and controllable multi-layer separation of raster images. In practical workflows, designers typically generate and edit each RGBA layer…

Graphics · Computer Science 2025-11-26 Zihao Liu , Zunnan Xu , Shi Shu , Jun Zhou , Ruicheng Zhang , Zhenchao Tang , Xiu Li

Generative Models are a valuable tool for the controlled creation of high-quality image data. Controlled diffusion models like the ControlNet have allowed the creation of labeled distributions. Such synthetic datasets can augment the…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Joshua Niemeijer , Jan Ehrhardt , Heinz Handels , Hristina Uzunova

Layout generation aims to synthesize realistic graphic scenes consisting of elements with different attributes including category, size, position, and between-element relation. It is a crucial task for reducing the burden on heavy-duty…

Computer Vision and Pattern Recognition · Computer Science 2023-03-10 Mude Hui , Zhizheng Zhang , Xiaoyi Zhang , Wenxuan Xie , Yuwang Wang , Yan Lu

The conditional text-to-image diffusion models have garnered significant attention in recent years. However, the precision of these models is often compromised mainly for two reasons, ambiguous condition input and inadequate condition…

Computer Vision and Pattern Recognition · Computer Science 2024-03-28 Sicheng Li , Keqiang Sun , Zhixin Lai , Xiaoshi Wu , Feng Qiu , Haoran Xie , Kazunori Miyata , Hongsheng Li

Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality. However, the way to add new conditional controls to the pretrained CMs has not been explored. In this technical report, we consider…

Computer Vision and Pattern Recognition · Computer Science 2023-12-13 Jie Xiao , Kai Zhu , Han Zhang , Zhiheng Liu , Yujun Shen , Yu Liu , Xueyang Fu , Zheng-Jun Zha

Recent video generative models have demonstrated impressive visual fidelity, yet they often struggle with semantic, geometric, and identity consistency. In this paper, we propose a system-level framework, termed the Divide-and-Conquer…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Haoyu Zhao , Yuang Zhang , Junqi Cheng , Jiaxi Gu , Zenghui Lu , Peng Shu , Zuxuan Wu , Yu-Gang Jiang
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