给扩散模型戴上降噪耳机：只留信号，屏蔽噪声
扩散模型生成图像时，每一步都要从带噪图片里猜信号。但噪声和信号在频率上天然可分：低频是结构，高频是噪声。这篇论文发现，如果让模型在输入时就主动滤掉高频噪声（用可调的低通滤波器），它就能把算力集中在真正需要建模的低频信号上，而不是浪费在预测“噪声该长什么样”上。在ImageNet和商业模型SenseNova-U1上，这个无参数、即插即用的操作稳定提升了图像质量和评测分数。它不是你明天就能直接用的工具，但揭示了一个反直觉的事实：给AI“降噪”不是削弱它，而是帮它聚焦。
📄 原文摘要(英文)
Pixel-space diffusion models are trained on full-bandwidth noisy images, yet the useful signal available to the denoiser is strongly frequency dependent. Under rectified-flow diffusion and natural-image power-law spectra, the per-band data-to-noise contour k^{*}(t) = (1-t)^{-2/α} separates a signal-bearing low-frequency region from a noise-dominated high-frequency region at each time t. We show that this implicit coarse-to-fine structure is not merely descriptive: it induces a capacity-allocation problem. A standard pixel-space denoiser must discover the moving bandwidth boundary internally and can spend computation on frequency-time regions where the optimal prediction collapses to deterministic baselines rather than data-distribution modeling. To make this boundary explicit, we introduce Spectral Forcing, a parameter-free, time-conditional 2D-DCT low-pass operator applied to the noisy input before the patch embedder. Its cutoff expands monotonically with the diffusion time and becomes the identity at the data endpoint. Through controlled synthetic experiments, we identify the regime in which the operator is beneficial: coarse patch tokenization and data whose high-frequency content is predominantly noise rather than essential signal. On ImageNet-256 with JiT-700M/32, Spectral Forcing consistently improves both FID and Inception Score across different training epochs, demonstrating robust gains throughout training; at finer tokenization, the spectral forcing is still competitive. We further insert the unchanged operator into SenseNova-U1, a unified text-to-image model, where it improves DPG-Bench and GenEval, showing that the input-side spectral prior transfers beyond class-conditional generation. These results suggest a route to capacity-efficient pixel-space diffusion by showing the signal and hiding the noise.