Applicability of spectrally guided per‑instance noise schedules to multi‑stage models

Investigate whether per‑instance diffusion noise schedules derived from an image’s radially averaged power spectral density (RAPSD)—as used to guide training and sampling in single‑stage pixel diffusion—can be effectively applied within multi‑stage generative pipelines, specifically latent diffusion models and distilled diffusion models.

Background

The paper introduces per‑instance, spectrum‑conditioned noise schedules for pixel‑space diffusion models and shows improved ImageNet generation quality at fewer denoising steps compared to single‑stage baselines. However, the best-performing systems in practice are often multi‑stage approaches such as latent diffusion and distilled models, which differ architecturally and procedurally from single‑stage pixel diffusion.

The authors explicitly raise the question of whether the same spectral principles and schedule construction can transfer to multi‑stage pipelines, noting prior work that studied differences between latent and RGB spectra.