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arXiv Machine Learning By Liang Yue, Eric A. Vance

Same Layout, Different Count: What Number Words Do Past Ten in Text-to-Image Models

Read the original on arXiv Machine Learning →

The study investigates how text-to-image models interpret number words beyond ten objects. By keeping the object type and initial noise constant, researchers varied the requested count and observed that models reliably follow the number only up to about ten items; beyond that, the count influence weakens and the image composition is largely governed by the coarse noise. Models such as SD3.5‑Medium and FLUX.1‑dev lose adherence after ten, while FLUX.2 and Qwen‑Image loosely track counts up to twenty‑five, yet no clear directional effect is achieved beyond the models’ limits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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