Colored Noise Diffusion Sampling (CNS) – Plug‑and‑Play Sampler for Diffusion Models

Colored Noise Diffusion Sampling (CNS) addresses the spectral bias inherent in diffusion models, where low‑frequency structure is resolved early and high‑frequency detail appears only at the end. Standard SDE solvers inject uniform white noise at every step, wasting stochastic energy on already resolved frequencies. CNS reconsiders SDE inference as a targeted energy transfer: at each step it measures the progress of each frequency band via a pre‑computed progress index γ(f, t) ∈ [0, 1] and dynamically routes injected noise to the bands with the largest remaining structural deficit. A strict global variance‑conservation constraint (mean β² = 1) guarantees the modified SDE still converges to the target distribution. The result is a strictly plug‑and‑play sampler substitution – the same model, the same number of steps, only the noise injection changes. CNS can be dropped into any existing pipeline without model or step adjustments, offering a straightforward way to improve high‑frequency detail and overall image quality.

The implementation is available on GitHub and includes a paper detailing the algorithmic approach. Users can integrate CNS by replacing their current sampler with the CNS node while keeping the same step count. The authors report noticeable improvements in detail without sacrificing speed or requiring additional computational resources.

CNS is ideal for practitioners looking to enhance image quality in diffusion workflows without modifying the underlying model or training new samplers.