DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models
Hongji Yang, Wencheng Han, Yucheng Zhou, Jianbing Shen
摘要
In this paper, we introduce DC (Decouple)-ControlNet, a highly flexible and precisely controllable framework for multi-condition image generation. The core idea behind DC-ControlNet is to decouple control conditions, transforming global control into a hierarchical system that integrates distinct elements, contents, and layouts. This enables users to mix these individual conditions with greater flexibility, leading to more efficient and accurate image generation control. Previous ControlNet-based models rely solely on global conditions, which affect the entire image and lack the ability of element-or region-specific control. This limitation reduces flexibility and can cause condition misunderstandings in multi-conditional image generation. To address these challenges, we propose both intra-element and inter-element Controllers in DC-ControlNet. The Intra-Element Controller handles different types of control sig- † Equal contribution * Corresponding author nals within individual elements, accurately describing the content and layout characteristics of the object. For interactions between elements, we introduce the Inter-Element Controller, which accurately handles multi-element interactions and occlusion based on user-defined relationships. Extensive evaluations show that DC-ControlNet significantly outperforms existing ControlNet models and Layoutto-Image generative models in terms of control flexibility and precision in multi-condition control. Our project website: https://um-lab.github.io/ DC-ControlNet/.
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引用它的顶会 Paper2
- HiCoGen: Hierarchical Compositional Text-to-Image Generation in Diffusion Models via Reinforcement LearningHongji Yang, Yucheng Zhou, Wencheng Han, Runzhou Tao 等CVPR 2026 · 被引用 4 次
- DynFusion: Rethinking Condition Fusion for Adaptive Multi-Conditional Text-to-Image GenerationZheng Fang, Lichuan Xiang, Xu Cai, Bing Wang 等CVPR 2026
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- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
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- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 被引用 6,759 次
- Scalable Diffusion Models with TransformersWilliam Peebles, Saining XieICCV 2023 · 被引用 5,568 次
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