Universal Guidance for Diffusion Models (2302.07121v1)

Abstract: Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work, we propose a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components. We show that our algorithm successfully generates quality images with guidance functions including segmentation, face recognition, object detection, and classifier signals. Code is available at https://github.com/arpitbansal297/Universal-Guided-Diffusion.

• The paper’s main contribution is a retraining-free algorithm that enables diffusion models to flexibly integrate diverse guidance modalities.
• It employs forward and backward guidance strategies to leverage clean data predictions and noise recycling, ensuring realistic output quality.
• Experiments with models like Stable Diffusion demonstrate effective integration of segmentation, face recognition, and object detection signals without compromising performance.

Universal Guidance for Diffusion Models

The paper presents an innovative solution to the limitations of conventional diffusion models, which traditionally require retraining when conditioned on modalities other than the ones they were specifically designed for, such as text. The authors propose a universal guidance algorithm for diffusion models that eliminates the need for model retraining when adapting to different guidance modalities. This work aims to enable a single diffusion model to be flexibly guided by a variety of modalities, including segmentation, face recognition, object detection, and classifier signals, without compromising performance or quality.

Key Contributions

The primary contribution of the paper is the introduction of an algorithm that provides universal guidance for diffusion models. This algorithm is not dependent on retraining and can be applied to any modality. The significant components of this method include:

1. Forward Universal Guidance: The authors leverage the predicted clean data from a denoising network to compute the guidance based on the intended modality. This approach maintains the diffusion model as a foundational element while applying alterations only at the output stage.
2. Backward Universal Guidance: To enhance the adherence to the guidance criteria, the backward guidance optimizes guidance within the framework of clean images and adapts them to the noisier initial conditions through linear transformations.
3. Self-recurrence: Through recycling noise at each timestep, this mechanism counteracts deviations from the manifold of natural images, ensuring the synthesis of realistic outputs.

The results indicate effective integration of multiple complex guidance criteria, including segmentation and facial recognition, into a single framework. This versatility was achieved without deteriorating the quality of the generated images, which remain aligned with user-defined constraints and prompts.

Numerical Results and Claims

The paper elucidates the effectiveness of the proposed algorithm through extensive experiments using models like Stable Diffusion. The universal guidance system proves capable of maintaining image quality while achieving a diversified set of prompts. Notably, the paper showcases the algorithm’s ability to generate realistic imagery guided by distinctive constraints, something conventional models would typically need retraining for.

Implications and Future Work

The implications of this research are substantial for practical applications in AI-driven image generation, where flexibility and adaptability to various input modes are increasingly demanded. The presented algorithm could significantly reduce computational costs associated with retraining models and facilitate more efficient resource utilization by applying a single model across different use cases.

Theoretically, this work opens avenues to explore further refinement in universal guidance methods, such as incorporating more intricate modalities or optimizing the speed of convergence for guided image outputs. Future developments may focus on reducing the computational load introduced by the self-recurrence mechanism and potentially extending the framework for even broader applications beyond image generation, such as video or audio synthesis.

In conclusion, this paper represents a notable advancement in the domain of guided diffusion models, offering a robust and versatile approach to high-quality modal adaptation without the need for model-specific retraining, paving the way for more agile and accessible AI model deployment.