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"