Image Interpolation with Score-based Riemannian Metrics of Diffusion Models

Shinnosuke Saito; Takashi Matsubara

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arXiv · 2504.20288

Image Interpolation with Score-based Riemannian Metrics of Diffusion Models

Shinnosuke Saito·Takashi Matsubara

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Abstract

Diffusion models excel in content generation by implicitly learning the data manifold, yet they lack a practical method to leverage this manifold - unlike other deep generative models equipped with latent spaces. This paper introduces a novel framework that treats the data space of pre-trained diffusion models as a Riemannian manifold, with a metric derived from the score function. Experiments with MNIST and Stable Diffusion show that this geometry-aware approach yields image interpolations that are more realistic, less noisy, and more faithful to prompts than existing methods, demonstrating its potential for improved content generation and editing.

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Shinnosuke Saito, Takashi Matsubara. 2025-04-28. Image Interpolation with Score-based Riemannian Metrics of Diffusion Models. https://arxiv.org/abs/2504.20288

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