arXiv: cs.AI preprintsInternational6 November 2026
Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution
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arXiv:2605.26032v2 Announce Type: replace-cross Abstract: 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 $\textbf{SKILD}$, a $\textbf{S}$cale-invariant $\textbf{K}$-Space $\textbf{I}$mage $\textbf{L}$earning $\textbf{D}$iffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and w
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