Minimal Impact ControlNet: Advancing Multi-ControlNet Integration

Summary

This article addresses the coordination of diverse control signals in diffusion models for precise manipulation, specifically focusing on the conflicts caused by low-frequency signals, termed "silent control signals." Such signals often interfere with those that generate finer details, resulting in suboptimal outcomes. To tackle this, the authors propose the Minimal Impact ControlNet (MIControlNet) framework, which minimizes ControlNet’s influence on the original U-Net and mitigates conflicts through modified training data, multi-objective optimization, and adjustments to the Jacobian matrix asymmetry in the score function. A two-stage training process is used: first, balanced data and feature injection for various conditions, followed by a secondary phase incorporating a conservativity loss to enhance signal compatibility and image fidelity.

Strengths

1. Analyzed challenges in training diffusion models with multiple control signals for ControlNet: * Data Bias: Handling biases in regions influenced by "silent" control signals. * Optimal Combination Ratios: Determining ideal ratios for integrating multiple ControlNet layers. * Conditional Score Function Stability: Ensuring balanced, stable updates across control signals (referred to as the “conservativity” of the score function). 2. Introduced a conservativity loss function within a modular network structure, supported by theoretical analysis. 3. Results demonstrate that the new loss function improves texture details specifically in areas affected by silent control signals, achieving finer and more accurate generation.

Weaknesses

1. ControlNet is not yet a widely recognized concept. It is recommended that the authors briefly introduce its definition when first mentioned; otherwise, readers may feel confused starting from the abstract. 2. Some phrases lack clarity. For example, in lines 18-19 of the abstract, "..., the silent control signals can suppress the generation of textures in related areas,..." leaves "related areas" ambiguous. Does this refer to areas that are texturally similar, spatially close, or something else? 3. There is a lack of necessary citations, such as in lines 164-165, where it states "to find the Pareto optimal solution, a state where no single objective can be improved...". Adding citations here would provide more credibility to the explanation. 4. The organization of the paper could be improved, as it is currently somewhat challenging to follow. Additionally, the main text does not sufficiently emphasize results showing the balancing of coefficients among multiple control signals. 5. The three main issues highlighted in the problem analysis section lack clear support in the experimental results. 6. Ablation studies are missing, which would help illustrate the individual contributions of each component to the overall outcome.

Questions

* In Equations (8) and (9), what do v1 and v2 represent? Additionally, since λi varies across layers, should v1 and v2 also include subscripts to indicate properties at different layers within the network? * The concept of "conservativity" in ControlNet doesn’t seem to be a commonly used term. I find it somewhat confusing—could the authors provide further clarification? * In Figure 4(b), why is there no comparison with ControlNet? From the results, it appears that Stage-1 has a more significant impact than Stage-2. How do the authors view the balance between these two stages? This may relate to the previous question, as the authors mention that Stage-2 primarily enhances the "conservativity" of control signals. * Could this approach potentially be extended by first separating foreground and background for guided generation and then synthesizing the layers? This might allow for even greater control over specific elements.

Rating

6

Confidence

3

Soundness

2

Presentation

1

Contribution

2

Summary

In current ControlNet training, each control is designed to influence all areas of an image so that conflicts occur where lacking boundary information(low-frequency signals) especially in the black areas. The authors proposed the Minimal Impact ControlNet(MIControlNet) that can handle conflicts when different control signals are expected to manage different parts of the image. The introduced silent control signals which utilizes segmentation masks can rebalance the distribution of high-frequency information in the blank area. The authors reorganized the formula of ControlNet giving theoretical enhancement and ensuring more stable learning dynamics. The proposed multi-objective optimization strategies which utilizes Multiple Gradient Descent Algorithm (MGDA) improved the model performance.

Strengths

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 of ControlNet is equal to the gradient of the estimated conservativity loss and simplified loss for the ControlNet optimization are well explained. The proposed model achieves the best FIDs in most cases within the multi-control signals.

Weaknesses

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 version together between ControlNet and proposed model. For example, there are no comparison about Jacobian Asymmetry between the other baselines in Figure 4.(b). To claim the main contributions of each method, it would be beneficial to compare the performance improvements of each method against ControlNet. The problem settings and the flow of the solutions are well-structured, however, the segmentation masking and the feature injection process shown in Figure 3 seems to be a little simple. Although the two main contributions(silent control signals, feature injection method) enhanced the model performance in the targeted problems, the method to apply the techniques are not quite novel. Minor: In Figure 6, the legend seems to have lack of information.

Questions

In line 121, $f^e_c$ seems weird. Doesn't the value under $f$ mean layer number?

Rating

6

Confidence

2

Soundness

4

Presentation

3

Contribution

3

Summary

- The paper introduces Minimal Impact ControlNet (MIControlNet), a novel framework designed to refine the integration of multiple control signals within diffusion models for image generation. - The innovative approach of MIControlNet includes rebalancing data distribution, multi-objective feature combination, and reducing asymmetry in the Jacobian matrix to minimize conflicts between control signals. - Experimental results demonstrate that MIControlNet outperforms baselines, achieving better compatibility and fidelity in generated images with multiple control signals.

Strengths

- 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.

Weaknesses

- 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.

Questions

See Weaknesses.

Rating

6

Confidence

4

Soundness

4

Presentation

3

Contribution

3

Summary

This paper presents a well-designed approach to multiple CtrlNet integration by addressing the challenge of control signal conflicts through a "minimal impact" strategy. Specifically, the authors address several issues they found in multi-ControlNet integration. 1. To handle data bias in silent control regions, they use data augmentation and segmentation masks. This adds texture to low-signal areas. 2. To avoid conflicts between control signals, they use an MGDA-inspired feature injection method. This balances gradients so that no single control dominates. 3. They introduce a conservativity loss function to ensure stable training. This function keeps CtrlNet's influence to original U-Net minimal and improves consistency in multi-control outputs.

Strengths

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.

Weaknesses

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 ControlNet performance, regardless of whether there is one or multiple CtrlNets in play. I wonder if the solutions proposed in this context could also enhance performance in standard single-CtrlNet applications. It might be beneficial for the authors to discuss this potential in the paper.

Questions

Please refer to the *weakness* section.

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Rebuttal to all reviewers

## Common Concerns 1: More experiments. **1. Calculate FID and Total Variance For ControlNet, MIControlNet(1-stage) and MIControlNet(2-stage).** Below is the table presenting the FID and Total Variance (TV, in units of $1\times 10^4$) for Canny and OpenPose conditions under three scenarios: no prompts, brief prompts, and detailed prompts. The following summary tables provide an overview of the results. | **(FID, TV)** | **ControlNet** | **MIControlNet(1-stage)** | **MIControlNet(2-stage)** | | ----------------------------- | ---------------- | ------------------------- | ------------------------- | | **Canny No Prompts** | 109.6, 2.62 | 114.4, 3.54 | **123.9, 3.79** | | **Canny Brief Prompts** | 89.34, 2.48 | 89.77, 3.24 | 90.18, 3.15 | | **Canny Detailed Prompts** | 88.55, 2.47 | 90.21, 3.28 | 89.37, 3.36 | | **OpenPose No Prompts** | 132.5, 3.34 | 131.9, 3.39 | **133.0, 3.67** | | **OpenPose Brief Prompts** | 97.08, 2.52 | 98.32, 2.71 | 98.14, 2.74 | | **OpenPose Detailed Prompts** | 99.09, 2.70 | 95.34, 2.92 | 94.16, 2.91 | We have the following findings: * MIControlNet, with or without the conservativity loss, **demonstrates similar FID performance**. However, **with conservativity loss, MIControlNet exhibits improved pattern generation ability** under silent control signals, **as highlighted in the table above.** * MIControlNet achieves **comparable FID performance** to the baseline but **demonstrates significantly stronger performance in terms of total variance.** * When comparing **no prompts**, **brief prompts**, and **detailed prompts**, **providing more detailed prompts generally leads to better FID performance and smaller total variance.** * Interestingly, **for detailed prompts, the total variance tends to slightly increase**. We hypothesize that this is due to the **more detailed prompts offering finer control under silent control signals**, thereby generating more diverse patterns. **2. The $Asym$ for Regular ControlNet** We calculate the $Asym$ for Regular ControlNet and compare it with our MIControlNet(1-stage) and MIControlNet(2-stage), the results are shown in the following table: | Condition | Canny | Hed | OpenPose | | ---------------------- | ------ | ------ | -------- | | ControlNet | 56.75 | 22.41 | 6.454 | | MIControlNet (1-stage) | 29.87 | 38.28 | 3.980 | | MIControlNet (2-stage) | 0.1174 | 0.1894 | 0.0274 | We have the following findings: * MIControlNet (1-stage) performs slightly better than ControlNet in terms of $Asym$. * MIControlNet (2-stage) significantly outperforms both ControlNet and MIControlNet (1-stage) on $Asym$. * For each condition, MIControlNet(2-stage) demonstrates consistent improvements on a logarithmic scale. ## Common Concerns 2: A more clear ablation. The FID for a throughly ablation: | Method | Openpose-Canny | Canny-Hed | Hed-Depth | | ------------------------------------- | --------------------- | --------------------- | --------------------- | | Vanilla ControlNet | 80.37 / 111.30 | 123.59 / 86.43 | 91.98 / 86.25 | | + Data Augmentation | 92.98 / 84.02 | 77.02 / 75.46 | 74.28 / 81.16 | | + Our Feature Injection & Combination | 76.13 / 77.22 | 74.19 / 70.26 | 71.16 / 71.93 | | + Conservativity Loss | **75.77** / **72.25** | **71.34** / **69.35** | **69.68** / **71.18** | We observe the following: * Our silent control signal-targeted data augmentation, feature injection & combination, and conservativity loss all lead to improvements in FID scores. * The conservativity loss, particularly for Canny combined with other conditions, achieves a consistent improvement of approximately 3 points in FID. * The improvements achieved through the conservativity loss are consistent, and we have further strengthened its theoretical foundation, particularly in the context of modular neural networks designed to **optimize GPU memory usage and computational efficiency**.

Rebuttal to all reviewers

We sincerely thank all the reviewers for their invaluable feedback and contributions. We also extend our apologies for the delayed response. * We extend our heartfelt thanks to reviewer ESQy for recognizing the clarity and systematic nature of our solution, as well as the robustness of our comprehensive experiments. Their valuable input on refining the experiments for single conditions has significantly enhanced the completeness of this manuscript. Additionally, **their insightful suggestions have provided us with valuable direction for exploring future work.** * We are deeply grateful to reviewer ZUxW for recognizing the significance of the issues we aim to address and acknowledging the performance of our methods. **Their valuable assistance in conducting experiments across detailed, brief, and null conditions has greatly enhanced the persuasiveness and robustness of our proposed approach.** * We sincerely thank reviewer DjNC for acknowledging the relevance of our problem settings and the soundness of our work, particularly the theoretical aspects. **Their suggestion to provide clearer ablation studies has greatly improved the clarity, comprehensibility, and overall completeness of this work.** * We extend our heartfelt gratitude to reviewer TnUC for recognizing the depth of our problem analysis and the theoretical contributions of our work. **Their suggestions helped us include additional details in the preliminaries and provide a clearer explanation of the conservativity loss**, enabling our work to reach and resonate with a broader audience. Thank you all very much again for your thoughtful and constructive feedback.