ControlNeXt: Powerful and Efficient Control for Image and Video Generation (2408.06070v2)

Abstract: Diffusion models have demonstrated remarkable and robust abilities in both image and video generation. To achieve greater control over generated results, researchers introduce additional architectures, such as ControlNet, Adapters and ReferenceNet, to integrate conditioning controls. However, current controllable generation methods often require substantial additional computational resources, especially for video generation, and face challenges in training or exhibit weak control. In this paper, we propose ControlNeXt: a powerful and efficient method for controllable image and video generation. We first design a more straightforward and efficient architecture, replacing heavy additional branches with minimal additional cost compared to the base model. Such a concise structure also allows our method to seamlessly integrate with other LoRA weights, enabling style alteration without the need for additional training. As for training, we reduce up to 90% of learnable parameters compared to the alternatives. Furthermore, we propose another method called Cross Normalization (CN) as a replacement for Zero-Convolution' to achieve fast and stable training convergence. We have conducted various experiments with different base models across images and videos, demonstrating the robustness of our method.

• The paper introduces a streamlined diffusion model with a lightweight convolutional module and Cross Normalization to enhance controllability and efficiency.
• It achieves up to 90% reduction in learnable parameters, significantly cutting training time and inference latency.
• The method offers plug-and-play integration with LoRA weights, enabling fast adaptation and stable convergence across diverse visual generation tasks.

ControlNeXt: Powerful and Efficient Control for Image and Video Generation

Introduction

"ControlNeXt: Powerful and Efficient Control for Image and Video Generation" introduces a novel method to enhance controllable image and video generation using diffusion models. Contemporary diffusion models exhibit significant prowess in generating high-fidelity visual data but face challenges in controllability and computational efficiency. ControlNeXt aims to address these challenges by offering a more streamlined and resource-efficient approach.

Methodology

Architectural Design

ControlNeXt presents an architectural simplification compared to existing methods like ControlNet. Instead of introducing computationally intensive branches to the diffusion model, ControlNeXt employs a lightweight convolutional module. This module, comprised of minimal additional parameters, extracts conditional control features and integrates them into the main denoising process at a mid-point block. This design minimizes computational overhead and allows seamless integration with existing Low-Rank Adaptation (LoRA) weights, enabling style alterations without further training.

Cross Normalization

The paper introduces Cross Normalization (CN) as a pivotal innovation. While zero convolution provides a stable introduction of new parameters, it slows down convergence due to its incapability to immediately influence the model. CN, however, normalizes the outputs of the control features using the mean and variance from the denoising branch, ensuring aligned data distributions. This technique enhances training stability and accelerates convergence, thereby mitigating the "sudden convergence" issue commonly observed in controllable generation tasks.

Experimental Results

The experiments conducted validate ControlNeXt's efficiency and effectiveness across various tasks and backbones like Stable Diffusion 1.5, Stable Diffusion XL, and Stable Video Diffusion. The method demonstrated robust performance in both image and video generation tasks, supporting different types of conditional controls including masks, depth, Canny edges, and pose sequences. Notably, ControlNeXt achieved up to 90% reduction in learnable parameters compared to existing methods, significantly reducing training and inference latency with minimal overhead.

Training Convergence

ControlNeXt exhibited faster training convergence compared to ControlNet. The method required only a few hundred steps to start fitting conditional controls, significantly reducing the training duration and addressing the sudden convergence problem effectively. This efficiency is attributed to the Cross Normalization technique, which ensures that new parameters can start influencing the model early in the training process.

Efficiency and Plug-and-Play Capability

In terms of parameters, ControlNeXt adds a negligible overhead, maintaining a lightweight profile that enhances its practicality and usability. The inference time benchmarks show that ControlNeXt incurs merely a minimal increase in latency compared to the base models. Moreover, ControlNeXt's design allows it to function as a plug-and-play module. It integrates effortlessly with different LoRA weights, enabling style modifications without any additional training, as demonstrated with models like AnythingV3 and DreamShaper.

Implications and Future Work

The implications of this research are notable for both the theoretical and practical advancements in AI-generated content (AIGC). The efficiency and robustness of ControlNeXt make it highly desirable for applications requiring quick adaptation to new styles and controls without extensive retraining. The introduction of Cross Normalization could influence further research in training large models, providing a method to seamlessly integrate additional parameters without compromising stability or convergence speed.

Future developments could explore the extension of ControlNeXt's principles to other domains beyond image and video generation. Investigating integration with more extensive datasets and diverse conditional controls could further validate and refine its applicability. Additionally, future work could combine Cross Normalization with other parameter-efficient fine-tuning (PEFT) methods to enhance model adaptability and performance across varying tasks.

Conclusion

The "ControlNeXt: Powerful and Efficient Control for Image and Video Generation" paper presents an innovative approach to controllable diffusion models. By simplifying architectural design and introducing Cross Normalization, ControlNeXt achieves remarkable efficiency and robust performance, significantly advancing the state-of-the-art in controllable visual generation. With its lightweight, plug-and-play nature and fast convergence, ControlNeXt sets a promising direction for future research and practical applications in the field of AI-generated visual content.