Minimal Impact ControlNet: Advancing Multi-ControlNet Integration
Shikun Sun, Min Zhou, Zixuan Wang, Xubin Li, Tiezheng Ge, Zijie Ye, Xiaoyu Qin, Junliang Xing, Bo Zheng, Jia Jia
TL;DR
This paper introduces Minimal Impact ControlNet, a method that improves multi-control signal integration in diffusion models by reducing conflicts and enhancing compatibility among control signals, especially for silent control regions.
Contribution
It proposes a novel approach with three strategies to mitigate control conflicts, enabling more harmonious multi-control image generation in diffusion models.
Findings
Enhanced control signal compatibility in multi-ControlNet setups
Reduced suppression of textures in silent control regions
Improved image generation quality with multiple control signals
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…
Peer Reviews
Decision·ICLR 2025 Poster
The solution proposed is clear and systematic. Experiments are comprehensive and convincing, covering various control signal combinations and providing robust quantitative and qualitative results. Limitation is discussed in the appendix.
Based on the methods presented, it appears that the proposed framework effectively supports scenarios involving more than two CtrlNets. Did the authors conduct experiments to evaluate performance in such cases? Since the experiments primarily focused on pairs of CtrlNets, I am curious about how well the approach scales to accommodate multiple CtrlNets. Additionally, the authors mention the "Conservativity of Conditional Score Function", which seems to be a general principle that influences Cont
- The method improves the compatibility of multiple control signals, which is crucial for applications requiring simultaneous control from different sources. - By addressing conflicts between control signals, the approach leads to higher quality image generation, particularly in areas with silent control signals.
- In Figure 1, why is it not the canny signal that suppresses the openpose signal? Is there some analysis? - The authors should supplement corresponding **prompts**. Detailed prompts often lead to the generation of complex textures. Comparing the total variance and visual results under detailed/brief/null text conditions would enhance the persuasiveness of the proposed method in this paper. A good response to the Weaknesses will improve my initial rating.
The problem settings and overall paper structure are well organized. The qualitative results for both single control signal and multi control signals utilizing segmentation masking are well aligned with handling the distribution of high-frequency information in the blank area. The blur effect shows in ControlNet even with single condition generation in silent area, however the MIControlNet shows more high-frequency informations in silent area. The proof that gradient of the conservativity loss
In 2.2, the notations and symbols are confused and hard to read. The proposed estimated conservativity loss function needs to construct second order derivatives, which is computationally expensive. The authors proposed three main contributions with rebalancing the data distribution, feature injection and combination, and conservativity loss, however there are no ablation studies at each component in the paper. The only comparison is that both the original feature combination and a balanced ver
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Neural Networks and Applications · Advanced Data Processing Techniques