When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models

TL;DR

NUMINA is a training-free framework that enhances numerical accuracy and alignment in text-to-video diffusion models by identifying and guiding layout consistency, significantly improving counting accuracy and CLIP alignment.

Contribution

Introduces NUMINA, a novel identify-then-guide method that improves numerical alignment in text-to-video diffusion without additional training.

Findings

Up to 7.4% improvement in counting accuracy on CountBench.

Enhanced CLIP alignment and temporal consistency.

Effective structural guidance complements existing prompt techniques.

Abstract

Text-to-video diffusion models have enabled open-ended video synthesis, but often struggle with generating the correct number of objects specified in a prompt. We introduce NUMINA , a training-free identify-then-guide framework for improved numerical alignment. NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout. It then refines this layout conservatively and modulates cross-attention to guide regeneration. On the introduced CountBench, NUMINA improves counting accuracy by up to 7.4% on Wan2.1-1.3B, and by 4.9% and 5.5% on 5B and 14B models, respectively. Furthermore, CLIP alignment is improved while maintaining temporal consistency. These results demonstrate that structural guidance complements seed search and prompt enhancement, offering a practical path toward count-accurate text-to-video diffusion. The code is available at https://github.com/H-EmbodVis/NUMINA.