给AI的提示词越有条理，生成图越听话
给AI的提示词越有条理，生成图越听话。研究者发现，扩散模型的训练损失并不随提示词长度变化，而是随提示词里“结构化语言”的量变化——也就是那些有明确语义和几何关系的描述，比如“红球在蓝方块左边”。他们用两个指标量化这种结构化程度，发现损失随其一线性下降、随另一个呈幂律下降。基于这个规律，他们造了一个“提示器”，能从图片自动生成结构化提示词，再用这些提示词训练生成模型，结果在几乎所有组合、推理和世界知识基准上都超过了开源模型，多数评测也追平或超过最强闭源模型。它不是你明天能用上的，但解释了为什么“描述得越具体、越有逻辑，AI画得越准”这个直觉是对的。
📄 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.