Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model

Authors: Han Lin, Jaemin Cho, Abhay Zala, Mohit Bansal
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Adapter, Diffusion, ControlNet, Text-to-video Generation, Image-to-video Generation, Text-to-image Generation

Abstract:
ControlNets are widely used for adding spatial control to text-to-image diffusion models. However, when it comes to controllable video generation, ControlNets cannot be directly integrated into new backbones due to feature space mismatches, and training ControlNets for new backbones can be a significant burden for many users. Furthermore, applying ControlNets independently to different frames can not effectively maintain object temporal consistency. To address these challenges, we introduce Ctrl-Adapter, an efficient and versatile framework that adds diverse controls to any image/video diffusion models through the adaptation of pretrained ControlNets. Ctrl-Adapter offers strong and diverse capabilities, including image and video control, sparse-frame video control, fine-grained patch-level multi-condition control, zero-shot adaptation to unseen conditions, and supports a variety of downstream tasks beyond spatial control, including video editing, video style transfer, and text-guided motion control. With six diverse U-Net/DiT-based image/video diffusion models (SDXL, PixArt-α, I2VGen-XL, SVD, Latte, Hotshot-XL), Ctrl-Adapter matches the performance of pretrained ControlNets on COCO and achieves the state-of-the-art on DAVIS 2017 with significantly lower computation (< 10 GPU hours).

Decision: accept (oral)
Reviewer scores: 8, 6, 6, 8

Reviews and discussion:
<thread>
creation date:
1730159076408

thread_type:
official_review

<thread_object>
creator: reviewer_pa78

<content>
summary:
This work Ctrl-Adapter extends X-Adapter to a more general setting: the pretrained ControlNet and downstream diffusion models can now differ in modality (images, videos) and architecture (e.g., UNet or Transformers). The authors show in extensive experiments that Ctrl-Adapter works well in many scenarios and outperforms X-Adapter.

strengths:
- The major technical contribution is to extend X-Adapter by involving temporal convolutions for the support of adapting from image to video. 

- There are a bunch of engineering contributions to improve performance: (1) the utilization of Q-former; (2) skipping latents; (3) handling noise scheduling shift.

- A really extensive experiments to demonstrate the strength of their work in various scenarios: single condition, multi conditions, zero-shot, from image to image, from image to video.

weaknesses:
1.	Overall, the writing could be improved to enhance clarity. The methodology section should be more focused and closely aligned with the key contributions. Currently, it is cluttered with engineering details and lacks mathematical formulation, which makes it harder to grasp the technical novelty.

2.	Does skipping latent require retraining ControlNet? 

3.	The Multi-ControlNet setup is unclear:
	•	Are six conditions trained simultaneously in each iteration?
	•	In Section 4.5, Figure 8 (zero-shot results), the zero-shot inferences under different conditions do not appear to align accurately with the intended conditions, which is understandable since they were not trained on these. However, could you provide an example without ControlNet for comparison? Could you also add quantitative results for the zero-shot experiment in Table 4? (Consider adding a row for models trained solely on depth but applied to other conditions, and a row with plain diffusion without ControlNet as a baseline for comparison.)

4.  What is architecture difference with X-Adapter? What is the training speed improvement over X-Adapter come from?

questions:
See weakness part. Overall, I lean to accept the paper. I need more explanation compared to X-Adapter in the technical side: (1) the architecture difference; (2) the training difference; (3) explain what is the performance and training speed come from.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730656985030

thread_type:
official_review

<thread_object>
creator: reviewer_qcbv

<content>
summary:
CTRL-Adapter is an efficient and versatile framework designed to integrate diverse controls into image and video diffusion models by adapting pretrained ControlNets. This method achieves comparable performance to training ControlNet from scratch while significantly reducing training costs. It introduces fine-grained, patch-level MoE (Mixture of Experts) routing to compose ControlNet features more effectively, as opposed to prior methods that operate only at the image or frame level. CTRL-Adapter is compatible with both UNet-based and DiT-based backbones, continuous timestep samplers, and varying noise scales. Extensive experiments demonstrate that CTRL-Adapter performs well on image and video generation and can be applied to various tasks, including video editing, style transfer, and text-guided object motion control.

strengths:
1. The proposed method stands out for its simplicity and efficiency. It presents a cost-effective approach to adapting image-based ControlNet models for video-based applications. It also offers valuable insights into merging multiple conditions using three strategic choices. It incorporates a fine-grained, patch-level Mixture of Experts (MoE) routing to enhance the generation process by enabling precise and adaptive control over features.

2. The evaluation is thorough and well-executed, providing extensive comparisons across both single-condition and multi-condition video generation. Additionally, it demonstrates the model's versatility by showcasing its performance on tasks such as video editing, style transfer, and text-guided object motion control, highlighting its wide applicability.

3. The paper is exceptionally well-written, with a logical and coherent structure that ensures ease of understanding.

weaknesses:
1. The comparison of training costs might not be entirely fair. Since the CTRL-Adapter leverages a pre-trained image ControlNet and then adapts it for video tasks, it benefits from the substantial pre-training cost in the image control task. Given that the image ControlNet is already proficient at extracting information from various conditions, this adaptation should inherently be faster. Therefore, when evaluating training efficiency, it is crucial to account for the advantages gained from the pre-trained ControlNet. 

2. The explanation of how multiple conditions are integrated in the proposed method could be clearer and more detailed. The paper briefly outlines three possible integration strategies in a single paragraph, accompanied by a figure, which might be insufficient for comprehending the intricacies involved. This section deserves more space and elaboration in the main text rather than being relegated to the appendix.

questions:
1. What is the additional inference cost associated with using multiple condition ControlNets along with their corresponding control adapters, for example, when handling seven different conditions in Table 3? While the paper mentions significant reductions in training costs by focusing on adapting image-based ControlNets for video tasks, how does the increase in inference complexity, due to maintaining multiple controls, impact overall efficiency and performance? 

2. Would integrating control adapters directly inside each ControlNet simplify the adaptation process and make it more efficient, like a fine-tuning task from image to video ControlNet? Could this approach be a viable and efficient baseline, particularly for single-condition scenarios?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730702817962

thread_type:
official_review

<thread_object>
creator: reviewer_isfd

<content>
summary:
This paper introduces a new framework called CTRL-Adapter, aimed at enhancing the control capabilities of image and video diffusion models. By adapting pretrained ControlNets, CTRL-Adapter allows for the efficient integration of various control conditions into new diffusion models, supporting image control, video control, and sparse frame control. The results demonstrate that CTRL-Adapter maintains high performance on various datasets with significantly reduced computational costs compared to training ControlNets from scratch.

strengths:
+ The presentation and writing quality of this manuscript are are generally strong and well-structured.
+ The framework supports a wide range of applications, from image and video control to video editing and style transfer.
+ CTRL-Adapter requires fewer computational resources. This method significantly reduces the training time, outperforming baselines in less than 10 GPU hours
+ The authors provide comprehensive experiments that validate the performance of CTRL-Adapter.

weaknesses:
+ While skipping latents can increase efficiency, it may inadvertently lead to loss of fine-grained detail in frames without direct conditioning. I'm wondering if there are any issues with this method that I'm concerned about?
+ Providing more detailed results from an ablation study about Weaknesses 1 could offer valuable insights into the impact of this strategy. It could reveal conditions where skipping latents is beneficial versus scenarios where it may compromise quality.
+ Whether skipping latents reduces the ability of the CTRL-Adapter to generalize between certain control types.
+ In my experience, the most significant advantage of this work is the value of a wide range of applications. In terms of technical contributions, it seems to extend multiple adapters in various generative models. Could the authors provide more elaboration on the technical contribution or insights?

questions:
Please see weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1730722980633

thread_type:
official_review

<thread_object>
creator: reviewer_6nvp

<content>
summary:
The paper proposes an efficient method for adapting pretrained ControlNets to various image and video diffusion frameworks. The authors also proposed a mixture of expert method for multi conditioning, and a mapping between discrete and continuous diffusion timesteps. Overall, the paper address an important problem (adapting pre-trained ControlNets efficiently), provide useful insights and achieves superior results.

strengths:
- The results proposed in the paper are interesting and convincing. 
- Adapting pre-trained ControlNets to new diffusion frameworks including videos is interesting and the method is novel. 
- The paper is well written and the experiments to validate the proposed method are significant. 
- The code is provided.

weaknesses:
- Poor presentation: the main paper is packed with experiments but lacks many essential details about the method such as the adapter architecture, more details about the MoE method, etc. 
- Unjustified architecture choices: the authors proposed to use a 1-1 mapping between the ControlNet layer to ones with similar dimension in the new diffusion model. This assumes that these layers learn similar features but the authors did not touch on this topic or justified their choices. 
- What is the rational behind conditioning the CTRL-Adapter with the first frame? How does the proposed method perform without such conditioning?

questions:
Additional comments (no need to address):
- typo in in L069 "Controlnet a cannot"
</content>
</thread_object>

</thread>
<thread>
creation date:
1732155724071

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General comments

comment:
We would like to sincerely thank all the reviewers for their valuable feedback and suggestions. We have provided responses to all of the reviewers' questions in the individual rebuttals. 

Following the reviewers’ suggestions, we restructure the method section in the **revised PDF (updated content is colored blue)** with the following main changes, with all other content **unchanged** if not colored in blue:
- (1) more details about the **adapter architecture** in Sec. 2.2, and **mathematical formulation** of our training loss in Sec. 2.1.
- (2) expanded the paragraph “Flexible adaptation to different backbones and settings” into three new independent paragraphs (i.e., adaptation to DiT, skipping latents, and inverse timestep sampling) **to align with our key technical contributions** in Sec. 2.2. 
- (3) More details on the architecture and intuition of our design choice for the three **MoE router** variants in Sec. 2.3. 

Additionally, we have removed the qualitative experimental results demonstrating adaptation to different downstream tasks from the main paper, and instead put the implementation details in the appendix.
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper introduces the CTRL adapter that adapts the pre-trained ControlNets to the new backbones in image and video generation. It allows to use a mixture of ControlNets for conditioning on multiple control signals. The authors provide comprehensive experiments on multiple applications, such as video editing, style transfer, and text-guided object motion control. The paper addresses an important problem of incorporating controls into image/video generative models in the computationally-efficient manner. I recommend the paper for Accept (Oral).