Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion
In this paper, we study the diffusability (learnability) of variational autoencoders (VAEs) in latent diffusion. We first derive distribution-free bounds on the maximum achievable reduction in pixel-space denoising loss. Under a power-law power spectral density (PSD), these bounds establish an ordering between sufficiently separated frequencies, with larger achievable reductions at lower frequencies. Motivated by this analysis and measurements of a trained pixel diffusion model, we seek to make low-frequency denoising gains useful for generating perceptually meaningful semantic content in latent diffusion. We propose the Spectrum Matching Hypothesis: latents with superior diffusability sh...
| Authors | Mang Ning, Mingxiao Li, Le Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 15 October 2026 |
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