Image Quality Emerges from the Gaussianity of Diffusion Inversion

Diffusion models learn to generate natural images by transforming Gaussian noise into samples from a complex image distribution. While previous studies mainly exploit this forward generation process, we ask whether the inverse trajectory can reveal information about the observed image itself. We discover that diffusion inversion provides a measurable signal of observation–prior compatibility: high-quality images that better agree with the learned image distribution tend to recover terminal latents with Gaussian-like statistics, whereas low-quality images induce increasingly atypical latent distributions. Based on this finding, we introduce Diffusion Inversion Gaussianity Score (DIGS), a training-free and quality-label-free measure that evaluates image quality by quantifying the deviation of inversion latents from the intrinsic Gaussian prior. Beyond marginal statistics, we further characterize channel-wise and spatial deviations to obtain DIGS${}^{\ast }$. Extensive experiments demonstrate that diffusion inversion statistics correlate strongly with human perceptual judgments across authentic and synthetic distortions, while requiring no quality annotations, reference images, or additional training. Our results reveal diffusion inversion as a new measurement space for probing how observations interact with learned generative priors.