DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models

2025-02-20

AI Summary

Existing ControlNet variants rely solely on global conditioning for multi-condition image generation, limiting element-level and region-level control precision and often causing conditional misinterpretation. To address this, we propose DC-ControlNet, a hierarchical control framework that decouples conditioning signals into intra-element and inter-element controllers: the former models individual object content and spatial layout, while the latter captures inter-object relationships and occlusion patterns. We further design a diffusion-based dual-path ControlNet architecture that jointly encodes semantic, geometric, and relational conditions, enabling fine-grained, cross-element conditional injection. Extensive experiments demonstrate that DC-ControlNet significantly outperforms state-of-the-art ControlNet variants and Layout-to-Image methods on multi-condition generation tasks, achieving substantial gains in both control flexibility and accuracy—thereby enabling robust, controllable synthesis of complex scenes.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Behavior Learning & Control

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 signals 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 Layout-to-Image generative models in terms of control flexibility and precision in multi-condition control.

Problem

• Decouple global control in image generation.
• Enhance element-specific control flexibility.
• Improve multi-element interaction accuracy.

Innovation

• Decouples control conditions
• Introduces Intra-Element Controller
• Introduces Inter-Element Controller