May 25, 2026 · cs.CVJ/K move · Enter open · S save
Zixin Jessie Chen, Zhuo Chen, Archer Wang, Jeff Gore+3
Department of Physics, Massachusetts Institute of Technology · NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Institute for Data, Systems and Society, Massachusetts Institute of Technology · Department of EECS, Massachusetts Institute of Technology
Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as reversing information loss across scales. We introduce
SKILD, a
Scale-invariant
K-Space
Image
Learning
Diffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and we leverage it to design a forward process that attenuates image content from fine to coarse scales while injecting spectrum-matched Gaussian noise, making scale an explicit coordinate of the diffusion dynamics. The same trained reverse process performs generation and continuous super-resolution by varying only the starting timestep:
no task-specific architecture, no conditioning branch, no classifier-free guidance, no retraining per scale factor. Empirically, SKILD reaches FID
2.65 and Inception Score
9.63 on unconditional CIFAR-10, performs
2×--
8× super-resolution on ImageNet from a single unconditional checkpoint while outperforming conditional models across perceptual metrics, and reconstructs critical Ising models whose connected four-point correlations closely track the ground truth.