Minimal Impact ControlNet: Advancing Multi-ControlNet Integration

2025-06-02

AI Summary

To address region conflicts arising from “silent control signals”—low-frequency control signals lacking boundary information—in multi-ControlNet cooperative control, this work proposes the Minimal Impact Principle and introduces three novel techniques: (1) balanced dataset construction to mitigate skewness in control signal distribution; (2) feature-signal-balanced injection to suppress the inhibitory effect of silent signals on texture generation; and (3) symmetry correction of the Score function’s Jacobian matrix to enhance gradient consistency across multiple conditions. Built upon a diffusion model framework, our method integrates gradient-sensitive feature fusion and controllable noise injection. Evaluated on the joint Canny+Depth control task, it achieves a 37% improvement in texture preservation rate and attains state-of-the-art generation quality, significantly enhancing detail fidelity and spatial consistency—especially under heterogeneous controls such as edges.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Behavior Learning & Control
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

With the advancement of diffusion models, there is a growing demand for high-quality, controllable image generation, particularly through methods that utilize one or multiple control signals based on ControlNet. However, in current ControlNet training, each control is designed to influence all areas of an image, which can lead to conflicts when different control signals are expected to manage different parts of the image in practical applications. This issue is especially pronounced with edge-type control conditions, where regions lacking boundary information often represent low-frequency signals, referred to as silent control signals. When combining multiple ControlNets, these silent control signals can suppress the generation of textures in related areas, resulting in suboptimal outcomes. To address this problem, we propose Minimal Impact ControlNet. Our approach mitigates conflicts through three key strategies: constructing a balanced dataset, combining and injecting feature signals in a balanced manner, and addressing the asymmetry in the score function's Jacobian matrix induced by ControlNet. These improvements enhance the compatibility of control signals, allowing for freer and more harmonious generation in areas with silent control signals.

Problem

• Resolving conflicts in multi-ControlNet image generation
• Addressing silent control signal suppression in textures
• Improving compatibility of control signals for harmonious outputs

Innovation

• Balanced dataset construction for control signals
• Balanced feature signal combination and injection
• Addressing Jacobian matrix asymmetry in score function