Generative Uncertainty in Diffusion Models

• URL: http://arxiv.org/abs/2502.20946v2
• Date: Thu, 12 Jun 2025 11:31:19 GMT
• Title: Generative Uncertainty in Diffusion Models
• Authors: Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt,
• Abstract summary: We propose a Bayesian framework for estimating generative uncertainty of synthetic samples.<n>We show that the proposed generative uncertainty effectively identifies poor-quality samples and significantly outperforms existing uncertainty-based methods.
• Score: 17.06573336804057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have recently driven significant breakthroughs in generative modeling. While state-of-the-art models produce high-quality samples on average, individual samples can still be low quality. Detecting such samples without human inspection remains a challenging task. To address this, we propose a Bayesian framework for estimating generative uncertainty of synthetic samples. We outline how to make Bayesian inference practical for large, modern generative models and introduce a new semantic likelihood (evaluated in the latent space of a feature extractor) to address the challenges posed by high-dimensional sample spaces. Through our experiments, we demonstrate that the proposed generative uncertainty effectively identifies poor-quality samples and significantly outperforms existing uncertainty-based methods. Notably, our Bayesian framework can be applied post-hoc to any pretrained diffusion or flow matching model (via the Laplace approximation), and we propose simple yet effective techniques to minimize its computational overhead during sampling.

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