给AI的提示词越有条理，画得越好
给AI画图的提示词，不是越长越好，而是越“有条理”越好。研究者发现，AI画图模型最终画得好不好，跟提示词里“结构化语言”的量成正比——也就是那些有明确逻辑、属性、空间关系的描述，而不是一堆形容词堆砌。他们用两个指标量化这种条理性，发现模型训练收敛后的损失随条理性线性下降。于是他们反过来用这个规律造提示词：从图片里自动提取语义和几何标注，拼成结构化提示，再训练一个“提示器”来生成这种提示。结果在几乎所有组合、推理、世界知识测试上，都超过了所有开源模型，大部分测试也追平或超过了最强闭源模型。
📄 原文摘要(英文)
We study empirical scaling properties for text conditioning in visual generation. Such properties have rarely been measured because diffusion loss does not scale with the number of tokens in natural-language prompts. Surprisingly, we find that the converged diffusion loss scales with the amount of structured language in the prompt. To quantify structured language, we adapt two complementary measures: a white-box likelihood metric (GPG) and a black-box attribute metric (ED). Across controlled training runs, the converged diffusion loss decreases approximately linearly with GPG and follows a power law with ED. Guided by these scaling properties, we improve diffusability by constructing structured prompts with semantic and geometric annotations derived from images, and improve promptability by training a prompter through supervised fine-tuning, cold-start, and verifier-gated on-policy distillation. The resulting system outperforms all evaluated open-weight models on nearly every compositional, reasoning, and world-knowledge benchmark, while matching or surpassing the strongest closed-weight models on most evaluations.