Selective Low-Frequency Recovery for Diffusion Guidance

Classifier-free guidance (CFG) improves conditional alignment in diffusion models, but high guidance scales can cause oversaturation, artifacts, and reduced sample diversity. Frequency-decoupled guidance (FDG) mitigates these effects by weakening low-frequency guidance while retaining stronger high-frequency guidance. However, uniformly suppressing the low-frequency residual can also attenuate information important for global structure and conditional consistency. Building on frequency-decoupled and projected guidance, we propose selective recovery guidance (SRG), a training-free method that restores low-frequency guidance according to its direction and denoising stage. SRG decomposes the low-frequency guidance residual into components parallel and orthogonal to the conditional low-frequency prediction. The method restores only the orthogonal component to a moderate guidance level while keeping the parallel component unchanged. This design preserves the projection of the conditional low-frequency prediction. A normalized log-SNR window confines the restoration to intermediate denoising stages, where structural predictions are reliable and global layout remains adjustable. The proposed method maintains original high-frequency guidance and requires no additional denoiser evaluations. Across five pretrained models, SRG improves conditional fidelity, prompt alignment and learned preference scores for text-to-image generation. On ImageNet and COCO 2017, it maintains FID, Precision and Recall comparable to FDG, preserving distributional quality and sample diversity. Ablations support the use of directional and temporal constraints with a moderate recovery strength. Selective low-frequency recovery improves the trade-off between conditional alignment and generation quality. Code is available at https://anonymous.4open.science/r/SRG-6E6D.