SKILD unifies unconditional image generation and continuous super-resolution in one diffusion model via scale-invariant k-space dynamics where the reverse process handles both tasks by varying only the starting timestep.
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Functional renormalization group applied to nearly continuous spectra yields a scale-dependent canonical dimension that undergoes a dimensional phase transition at signal-to-noise ratios below the BBP threshold, correlating with symmetry breaking and eigenvector deviations.
Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.
Flicker-DDPM accelerates DDPM sampling by injecting 1/f colored noise matched to image spectra, achieving similar quality with 3.33 times fewer steps on CIFAR-10.
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Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution
SKILD unifies unconditional image generation and continuous super-resolution in one diffusion model via scale-invariant k-space dynamics where the reverse process handles both tasks by varying only the starting timestep.