ControlNet-XS: Designing an Efficient and Effective Architecture for Controlling Text-to-Image Diffusion Models
(2312.06573)Abstract
The field of image synthesis has made tremendous strides forward in the last years. Besides defining the desired output image with text-prompts, an intuitive approach is to additionally use spatial guidance in form of an image, such as a depth map. For this, a recent and highly popular approach is to use a controlling network, such as ControlNet, in combination with a pre-trained image generation model, such as Stable Diffusion. When evaluating the design of existing controlling networks, we observe that they all suffer from the same problem of a delay in information flowing between the generation and controlling process. This, in turn, means that the controlling network must have generative capabilities. In this work we propose a new controlling architecture, called ControlNet-XS, which does not suffer from this problem, and hence can focus on the given task of learning to control. In contrast to ControlNet, our model needs only a fraction of parameters, and hence is about twice as fast during inference and training time. Furthermore, the generated images are of higher quality and the control is of higher fidelity. All code and pre-trained models will be made publicly available.
Overview
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Introduces ControlNet-XS, an efficient and effective architecture for text-to-image generative control.
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ControlNet-XS uses fewer parameters and runs approximately twice as fast as its predecessor.
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Utilizes zero-convolutions and is trained on one million images, outperforming competitors in various metrics.
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Addresses semantic biases by minimizing control model size, thus reducing unintended output influences.
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Acknowledges societal implications of advancements in text-to-image generation and the need for misuse prevention.
Introduction
In the realm of text-to-image generation, the integration of intuitive spatial guidance through controlling networks has become pivotal for steering the output towards a desired image. A controlling network allows the users to influence the image generation process using not just text-prompts but also guidance images like sketches or depth maps. This paper introduces an architecture named ControlNet-XS, a more efficient and effective successor to the known ControlNet, designed for controlling text-to-image diffusion models.
Improved Architecture and Performance
The proposed ControlNet-XS architecture stands out for requiring significantly fewer parameters than its predecessor while enhancing the quality and fidelity of control. Moreover, ControlNet-XS operates approximately twice as fast during inference and training, showcasing its efficiency. The paper details the problems with the delayed information transfer in existing controlling networks and the novel approach taken by ControlNet-XS to mitigate this issue.
Training Methodology and Evaluation
Trained on one million images from a dataset, ControlNet-XS uses zero-convolutions to prevent diminished generative capabilities of the controlled generative network at the start of the training. To evaluate performance, metrics like CLIP-Score, Learned Perceptual Image Patch Similarity (LPIPS), and Mean Squared Error for depth (MSE-depth) were used. ControlNet-XS outperformed competitors and showed that the model size could be reduced without significant performance losses.
Addressing Biases and Limitations
The research highlights the problem of semantic biases where large controlling networks may influence the generative model, inducing unintended output. ControlNet-XS addresses this by reducing the size of the control model, minimizing bias while maintaining high control efficiency. This approach resonates with the broader need for understanding and addressing biases within the AI-driven generative models.
Conclusion and Societal Impact
Concludingly, ControlNet-XS provides a significant advancement in controlled text-to-image generation with its method of efficient communication between the generative and controlling processes. With the provided code and pre-trained models, the work invites further innovation in the area. As these generative models advance, the paper acknowledges the societal implications, specifically the concerns around creating deep fakes, highlighting the necessity for ongoing research into misuse prevention and detection.