Noun Presence Loss with Dynamic Weighting for Compositional Text-to-Image Generation

Abstract

Compositional text-to-image generation requires faithful depiction of multiple objects with distinct visual attributes. While recent inference-time optimization methods have substantially improved attribute binding, entity neglect - where specified objects are absent from the generated image - remains an unresolved failure mode. We identify that existing syntax-guided attention objectives optimize for relative modifier-noun alignment but impose no constraint on the absolute activation magnitude of entity tokens, leaving neglected entities uncorrected even when binding objectives are satisfied. To address this, we propose a noun presence loss, a hinge-based objective that penalizes entity tokens with insufficient peak cross-attention activation during inference. A loss-ratio dynamic weight scales the presence loss proportionally to the binding loss magnitude at each timestep, preventing gradient conflict while maintaining stable optimization. Our method is training-free, requires no external components, and integrates with syntax-guided cross-attention optimization frameworks. Experiments on CC-500 and T2I-CompBench across color, shape, and texture categories demonstrate state-of-the-art performance on all metrics and attribute categories, confirming that entity presence optimization consistently improves compositional faithfulness and perceptual quality across diverse prompt types.