ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback
(2404.07987)Abstract
To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face significant challenges in generating images that align with the image conditional controls. To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency between generated images and conditional controls. Specifically, for an input conditional control, we use a pre-trained discriminative reward model to extract the corresponding condition of the generated images, and then optimize the consistency loss between the input conditional control and extracted condition. A straightforward implementation would be generating images from random noises and then calculating the consistency loss, but such an approach requires storing gradients for multiple sampling timesteps, leading to considerable time and memory costs. To address this, we introduce an efficient reward strategy that deliberately disturbs the input images by adding noise, and then uses the single-step denoised images for reward fine-tuning. This avoids the extensive costs associated with image sampling, allowing for more efficient reward fine-tuning. Extensive experiments show that ControlNet++ significantly improves controllability under various conditional controls. For example, it achieves improvements over ControlNet by 7.9% mIoU, 13.4% SSIM, and 7.6% RMSE, respectively, for segmentation mask, line-art edge, and depth conditions.
Overview
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ControlNet++ introduces pixel-level cycle consistency optimization to improve controllability in text-to-image diffusion models using conditional controls.
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The approach enhances fidelity to conditional controls like segmentation masks and line art through direct optimization, reducing computational demands.
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Experimental validations show ControlNet++ outperforming existing methods in terms of mean Intersection over Union, Structural Similarity Index Measure, and Root Mean Square Error.
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ControlNet++ opens new research avenues in personalized content creation and machine learning data augmentation, marking a significant advancement in generative AI.
ControlNet++: Enhancing Image-Based Controllability in Text-to-Image Diffusion Models
Introduction
The rapid progress in text-to-image diffusion models has significantly advanced the capabilities in generating detailed images from textual descriptions. However, the challenge of achieving precise controllable generation based on explicit image-based conditional controls persists. This paper introduces ControlNet++, a novel approach aimed at addressing the gap in generating images that align closely with conditional controls. By integrating a pixel-level cycle consistency optimization strategy, ControlNet++ significantly enhances the controllability of text-to-image diffusion models under various conditional controls.
Motivation and Background
The fidelity and detail of images generated from descriptive text have seen remarkable improvement, courtesy of advancements in diffusion models and the availability of large-scale image-text datasets. Despite these strides, the nuanced control over the generated image details through language alone remains an elusive goal. Methods like ControlNet have sought to augment text-to-image models with image-based conditional controls for improved generation accuracy. Nonetheless, the fidelity to these conditional controls often falls short, with existing models either requiring exhaustive computational resources for retraining or lacking precise control mechanisms.
ControlNet++ Approach
Addressing these challenges, ControlNet++ proposes a direct optimization of the cycle consistency loss between the input conditional controls and the conditions extracted from the generated images. This optimization leverages pre-trained discriminative models to enforce the fidelity of generated images to the specified controls, covering conditions such as segmentation masks, line art, and depth maps. The innovation lies in the efficient reward strategy that bypasses the need for multiple sampling steps by adding noise to input images and utilizing single-step denoised images for fine-tuning. This method significantly reduces the computational burden and enhances the model's ability to adhere to the given conditional controls.
Experimental Validation
Extensive experiments demonstrate the efficacy of ControlNet++ over existing methods, showing notable improvements across a range of conditional controls. For instance, relative to ControlNet, enhancements of 7.9% in mean Intersection over Union (mIoU), 13.4% in Structural Similarity Index Measure (SSIM), and 7.6% in Root Mean Square Error (RMSE) were observed for segmentation mask, line-art edge, and depth conditions, respectively. These results underscore ControlNet++'s superior capability in aligning generated images with the input conditions without compromising image quality.
Implications and Future Directions
The significant improvements in controllability introduced by ControlNet++ not only advance the state-of-the-art in text-to-image generation but also open up new avenues for research and application, including personalized content creation, enhanced interactive design tools, and more effective data augmentation techniques for machine learning training sets. Considering future developments, the exploration into expanding the range of controllable attributes and further optimizing the efficiency of feedback mechanisms presents a promising trajectory for advancing generative AI models.
Conclusion
ControlNet++ represents a significant advancement in the domain of text-to-image generation. By innovatively applying cycle consistency optimization through pre-trained discriminative models, it substantially improves the controllability under various image-based conditional controls. The efficient reward fine-tuning strategy not only preserves the quality of generated images but also ensures a computationally viable approach. This research lays the groundwork for further explorations into precise and efficient controllability mechanisms in generative AI.