GPT Image Text Rendering: Generated Posters Are Not Automatically Publishable

Evaluate text images for accuracy, layout, brand typography, language coverage, and editability.

Key takeawayText-image acceptance requires readability, accuracy, compliance, and editability—not merely a design-like appearance.

Why this deserves its own decision

Image models can render clearer headlines and labels, but prices, dates, discounts, legal lines, and brand names cannot rely on visual inspection alone. One character can invalidate an ad, and complex scripts or small text remain harder.

Decision framework

Keep critical copy as structured fields and compare it with OCR after generation.
Prefer adding brand fonts and legal fine print in a controlled layout layer.
Maintain separate tests for Chinese, English, numbers, and mixed-language copy.

Putting it into a ModelRush workflow

A ModelRush image task creates a text-free master or visual with placeholders, then OCR checks variable copy. A template engine overlays high-risk fields while low-risk decorative text may remain generated. Save prompt, copy, and template versions with the final file.