overhead. Through extensive experimentation, we demonstrate consistent
improvements in text alignment (CLIP Score), fidelity (FID, PFID), and
human-perceived quality (ImageReward). Our ablation studies validate each
design component, while user studies confirm significant preference for
NAG-guided outputs. As a model-agnostic inference-time approach requiring no
retraining, NAG provides effortless negative guidance for all modern diffusion
frameworks -- pseudocode in the Appendix!</p>
            
         