SAND: Stage-Aware Noise Decomposition for Training-Free Diffusion Guidance

Diffusion models have achieved state-of-the-art performanceacross 2D, video, and 3D generation, but their sampling quality remainsheavily dependent on guidance methods. Existing training-free guidancetechniques construct a weakened auxiliary branch through static per-turbations, yet these methods fundamentally disregard the stage-wisestructure of the denoising process: early timesteps govern magnitude-driven global formation, whereas later timesteps refine directional ge-ometry. We propose a principled training-free diffusion guidance withStage-Aware Noise Decomposition (SAND) that considers this temporalasymmetry by decomposing Gaussian noise into orthogonal magnitude-and direction-oriented components and modulating them with a time-adaptive weighting schedule aligned to diffusion dynamics. The decom-posed perturbations are injected directly into the self-attention queries,keys, and values, forming a dynamically degraded branch that requires noretraining while faithfully mirroring the evolving geometry of the sample.This representation-level perturbation provides a temporally coherentguidance signal that better balances global structure and fine detail. Ex-tensive experiments on Stable Diffusion XL (SDXL) and Stable Diffusion3 (SD3) demonstrate that our method consistently improves global co-herence, sharpens fine-grained texture, and yields superior sample qual-ity in both unconditional and conditional image generation. We furthershow that SAND can be extended to the guidance-distilled image gener-ation model FLUX.1 and the text-to-video model Wan2.2, highlightingits broad applicability.