Google AI researchers have proposed a novel framework for inference-time scaling in diffusion models, a class of deep generative models that have shown promising results in various applications, including image and video generation.
The proposed framework, called "Diffusion Scaling," provides a fundamental understanding of how diffusion models can be scaled at inference time, enabling more efficient and effective generation of high-quality samples.
Diffusion models are a type of probabilistic generative model that iteratively refine a random noise signal until it converges to a specific data distribution. However, scaling these models to generate high-quality samples can be computationally expensive and requires careful tuning of hyperparameters.
The Diffusion Scaling framework addresses these challenges by providing a theoretical foundation for understanding how diffusion models can be scaled at inference time. The framework introduces a new scaling parameter that controls the rate at which the diffusion process converges to the target distribution.
By adjusting this scaling parameter, users can trade off between sample quality and computational efficiency, enabling more flexible and efficient generation of high-quality samples.
The proposed framework has been evaluated on various diffusion models and datasets, demonstrating its effectiveness in improving sample quality and reducing computational costs.
The Diffusion Scaling framework has the potential to significantly impact the field of deep generative models, enabling more efficient and effective generation of high-quality samples for various applications.
