# 文生图提示词越长未必越好，结构化更关键

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-05 15:42
- AIHOT 分数：41
- AIHOT 链接：https://aihot.virxact.com/items/cmsfsymkc0d94rochbrvhkmek
- 原文链接：https://x.com/rohanpaul_ai/status/2084907993657975187

## AI 摘要

论文《Scaling Properties of Text Conditioning in Visual Generation》发现，文生图模型的效果受提示词对场景的暴露清晰度限制，而非长度；跨开源模型，单纯延长自然语言描述最终会使输出劣于最短描述。作者提出用结构化提示词，将场景、物体、边界框、深度、属性和关系分入命名字段，提示词工程应优化视觉变量的显式表达。

## 正文

Longer prompts are not what image generators need.

Text-to-image models seem less constrained by prompt length than by how clearly the prompt exposes the scene.

This paper finds that text conditioning scales with image-grounded information， not token count.

Across open-weight models， simply extending natural-language captions eventually made outputs worse than each model's shortest-caption result.

The authors replace prose with a structured prompt that separates the scene， individual objects， bounding boxes， depth， attributes， and relationships into named fields.

The shift here is： prompt engineering for visual generation should optimize how explicitly visual variables are represented， then train the prompter to fill that representation well.

The biggest prompt-engineering gain may come from how visual content is organized before it reaches the image model.

- arxiv. org/abs/2607.29679

Title： "Scaling Properties of Text Conditioning in Visual Generation"