Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
(2404.09967)Abstract
ControlNets are widely used for adding spatial control in image generation with different conditions, such as depth maps, canny edges, and human poses. However, there are several challenges when leveraging the pretrained image ControlNets for controlled video generation. First, pretrained ControlNet cannot be directly plugged into new backbone models due to the mismatch of feature spaces, and the cost of training ControlNets for new backbones is a big burden. Second, ControlNet features for different frames might not effectively handle the 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, by adapting pretrained ControlNets (and improving temporal alignment for videos). Ctrl-Adapter provides diverse capabilities including image control, video control, video control with sparse frames, multi-condition control, compatibility with different backbones, adaptation to unseen control conditions, and video editing. In Ctrl-Adapter, we train adapter layers that fuse pretrained ControlNet features to different image/video diffusion models, while keeping the parameters of the ControlNets and the diffusion models frozen. Ctrl-Adapter consists of temporal and spatial modules so that it can effectively handle the temporal consistency of videos. We also propose latent skipping and inverse timestep sampling for robust adaptation and sparse control. Moreover, Ctrl-Adapter enables control from multiple conditions by simply taking the (weighted) average of ControlNet outputs. With diverse image/video diffusion backbones (SDXL, Hotshot-XL, I2VGen-XL, and SVD), Ctrl-Adapter matches ControlNet for image control and outperforms all baselines for video control (achieving the SOTA accuracy on the DAVIS 2017 dataset) with significantly lower computational costs (less than 10 GPU hours).
Overview
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introduces a novel framework designed to enhance image and video diffusion models by integrating pretrained ControlNets for diverse spatial controls.
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Aims to address the challenge of applying pretrained image ControlNets directly to video diffusion models by ensuring temporal consistency across video frames.
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Demonstrates superior performance with significantly lower computational costs, supporting multiple conditions and backbone models.
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Validated through experiments, showing 's ability to match or outperform existing ControlNets in controlled image and video generation tasks.
Enhancing Video and Image Diffusion Models with Pretrained ControlNets: Introducing
Introduction to
The paper introduces , a novel framework designed to enhance existing image and video diffusion models by integrating pretrained ControlNets for diverse spatial controls. This advancement is crucial in addressing the limitation of directly applying pretrained image ControlNets to video diffusion models due to the mismatch of feature spaces and the high training cost associated with adapting ControlNets to new backbone models. The authors propose a solution that not only facilitates the adaptation process but also ensures temporal consistency across video frames.
Key Contributions
Framework Design: The framework is structured to train adapter layers that map pretrained ControlNet features to various image/video diffusion models without altering the ControlNet and backbone model parameters. This design choice significantly reduces the computational burden associated with training new ControlNets for each model.
Temporal Consistency: introduces temporal modules alongside spatial ones, addressing the challenge of maintaining object consistency across video frames. This feature is especially pivotal for applications that require precise control over video content.
Flexibility and Efficiency: The framework supports multiple conditions and backbone models, enabling it to adapt to unseen conditions efficiently. Remarkably, showcases superior performance with significantly lower computational costs compared to existing baselines.
Experimental Validation: Through extensive experiments, the authors demonstrate 's ability to match or outperform the performance of ControlNets in image and video control tasks on standard datasets like COCO and DAVIS 2017, achieving state-of-the-art video control accuracy.
Practical Implications
provides a robust method for adding spatial controls to diffusion models, making it highly beneficial for various applications such as video editing, automated content creation, and personalized media generation. The framework's compatibility with different backbone models and conditions, combined with its cost-effective training process, presents a significant advancement in controlled generation tasks. Additionally, 's capacity for zero-shot adaptation to unseen conditions and handling sparse frame controls showcases its adaptability and potential for future development in AI-driven content generation.
Future Directions
The introduction of opens multiple avenues for future research, particularly in improving the adaptability and efficiency of controllable generative models. Future works could explore further optimization of adapter layers for even lower computational costs or the integration of more sophisticated control mechanisms to enhance the quality and precision of generated content. Additionally, investigating the application of in other domains, such as 3D content generation and interactive media, could significantly broaden its utility.
Conclusion
presents a significant step forward in the development of efficient and versatile frameworks for controllably generating high-quality images and videos. By leveraging pretrained ControlNets and introducing novel adapter layers for temporal consistency, the framework addresses key challenges in the field and sets a new benchmark for future research.
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