text-to-image-word-order-matters-no-negation
IN premise — entries/2026/06/21/wiki-Prompt_engineering.md
Created 2026-06-21T09:50:10+00:00
In text-to-image models, word order matters (earlier words are weighted more heavily), negation is often not understood, and these models do not process grammar the same way LLMs do
Summary
When crafting prompts for image generators, the first words you write carry the most visual weight, and telling the model what to exclude (using "no," "without," or "not") often backfires because it just latches onto the concept itself. This means the system can't treat image prompting like natural conversation: it has to front-load the most important nouns and describe what should be present rather than what should be absent.
Dependents
These beliefs depend on this one:
- OUT multimodal-attention-safely-generalizes-across-domains — Attention's modality-agnostic universality and validated cross-modal deployment (ViT for images, AlphaFold for proteins) suggest that the Transformer safely generalizes as a universal computation substrate across new domains through tokenization alone.