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Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model (2404.09967v2)

Published 15 Apr 2024 in cs.CV, cs.AI, and cs.LG

Abstract: ControlNets are widely used for adding spatial control to text-to-image diffusion models with different conditions, such as depth maps, scribbles/sketches, and human poses. 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 cannot 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 model 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 (via an MoE router), 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-$\alpha$, 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).

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Authors (4)
  1. Han Lin (53 papers)
  2. Jaemin Cho (36 papers)
  3. Abhay Zala (10 papers)
  4. Mohit Bansal (304 papers)
Citations (7)
View on Semantic Scholar

Summary

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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