SCALE: Semantic-Calibrated Guidance Enhancement for Prompt-Faithful Diffusion

Ensuring prompt faithfulness remains a central challenge for text-to-image diffusion models. Classifier-Free Guidance (CFG) improves prompt adherence but exhibits an inherent quality–alignment tension: increasing the guidance scale to strengthen conditioning on the prompt often degrades visual quality and introduces artifacts. To probe the limit of greedy alignment maximization, we first introduce SAP (Semantic Alignment Projection), a greedy update rule that projects each sampling update onto the guidance direction to maximize per-step alignment progress. We then show that SAP can fail due to the loss of orthogonal corrective freedom, discarding high-dimensional components that are crucial for rectifying accumulated trajectory drift. Based on this diagnosis, we propose SCALE (Semantic-CALibrated Guidance Enhancement), a drop-in, training-free guidance mechanism. SCALE selectively amplifies the semantic component along the guidance direction while preserving the orthogonal corrective component, improving semantic alignment without compromising structural fidelity. Across multiple text-to-image benchmarks, SCALE delivers substantial and consistent gains in prompt adherence and compositional alignment with negligible overhead over standard sampling. Project page: https://github.com/SudongCAI/SCALE.