Diffusion models achieve state-of-the-art image synthesis, with their generative trajectories fundamentally exhibiting a spectral bias, resolving low-frequency global structures early and high-frequency fine details later. Conventional stochastic differential equation (SDE) solvers fail to account for this dynamic, naively injecting uniform white noise throughout the entire process and misusing the finite energy budget. In this work, we establish a mathematical framework that reconsiders SDE inference as a targeted, frequency-decoupled energy transfer. Leveraging this framework, we introduce Colored Noise Sampling (CNS), a novel, training-free stochastic solver. Rather than injecting uniform white noise, CNS utilizes a dynamic, timestep- and frequency-dependent schedule that more efficiently allocates injected energy toward structurally unresolved frequency bands. By actively exploiting the model's inherent spectral bias, CNS systematically steers the generated distribution toward the true data manifold. Extensive experiments demonstrate that CNS significantly outperforms standard ODE and SDE baselines as a strictly plug-and-play, inference-time sampler substitution across diverse architectures (SiT, JiT, FLUX). Compared to standard sampling on ImageNet-256, CNS achieves substantial unguided FID reductions, improving from 8.26 to 6.27 on SiT-XL/2, 32.39 to 26.69 on JiT-B/16, and 11.88 to 8.31 on JiT-H/16, while yielding consistent relative FID improvements with Classifier-Free Guidance. Project page is available at https://hadardavidson.github.io/CNS/.

翻译：扩散模型在图像合成中实现了最先进的性能，其生成轨迹本质上表现出谱偏差，即早期解析低频全局结构，后期处理高频细节。传统的随机微分方程求解器未能考虑这一动态特性，在整个过程中简单地注入均匀白噪声，并错误地使用有限能量预算。本研究建立了一个数学框架，将随机微分方程推理重新视为一种针对性的、频率解耦的能量传递。利用此框架，我们提出了彩色噪声采样（CNS），一种新颖的、无需训练随机求解器。不同于注入均匀白噪声，CNS采用动态的、与时间步和频率相关的调度策略，更高效地将注入能量分配给结构上未解析的频率波段。通过主动利用模型固有的谱偏差，CNS系统地引导生成分布趋向真实数据流形。大量实验表明，作为严格的即插即用推理阶段采样器替代方案，CNS在多种架构（SiT、JiT、FLUX）上显著优于标准常微分方程和随机微分方程基线。在ImageNet-256标准采样中，无需引导的FID分数大幅降低：SiT-XL/2从8.26提升至6.27，JiT-B/16从32.39提升至26.69，JiT-H/16从11.88提升至8.31，且在无分类器引导下取得一致的相对FID改进。项目页面：https://hadardavidson.github.io/CNS/。