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How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models.

Giulio Franzese1, Simone Rossi1, Lixuan Yang2

  • 1EURECOM Data Science Department, 06410 Biot, France.

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|May 16, 2023
PubMed
Summary
This summary is machine-generated.

This study optimizes score-based diffusion models by using smaller diffusion times, improving both training and sampling efficiency and quality. An auxiliary model bridges the gap in forward dynamics for better results.

Keywords:
diffusion modelsefficiencygenerative modellingvariational inference

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Generative Models

Background:

  • Score-based diffusion models generate data from noise using stochastic differential equations.
  • The role of diffusion time (T) in these models is not fully understood.
  • Current methods often use large T for noise distribution but smaller T offers better score-matching and efficiency.

Purpose of the Study:

  • To analyze the trade-off associated with diffusion time (T) in score-based diffusion models.
  • To propose a novel method for enhancing training and sampling quality and efficiency.
  • To investigate the impact of smaller diffusion times.

Main Methods:

  • A variational interpretation of diffusion models was employed.
  • An auxiliary model was introduced to bridge the gap between ideal and simulated forward dynamics.
  • Standard reverse diffusion process was applied after the auxiliary model step.

Main Results:

  • Quantified the trade-off between diffusion time, score-matching accuracy, and computational efficiency.
  • Demonstrated that smaller diffusion times can be effectively utilized with an auxiliary model.
  • Achieved competitive results on image data compared to state-of-the-art methods in terms of sample quality and log-likelihood.

Conclusions:

  • Optimizing diffusion time (T) is crucial for balancing model performance and efficiency.
  • The proposed auxiliary model approach effectively mitigates the limitations of shorter diffusion times.
  • This method offers a promising direction for more efficient and high-quality generative modeling.