# When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models Source: [When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models]() Collection index: [llms.txt]() Data updated: 2026-04-13T06:00:00.800Z Authors: Zhengyang Sun, Yu Chen, Xin Zhou, Xiaofan Li, Xiwu Chen, Dingkang Liang, Xiang Bai Published: 2026-04-09T17:59:57.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Text-to-Video Generation on CountBench](): Text-to-Video Generation; CountBench; Counting Accuracy: 0.591. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()