Never Bake Text Into AI-Generated Images

We asked Gemini to generate an Instagram carousel image with the caption “Top 5 Travel Tips ✈️” overlaid on a scenic background.

What we got back: “Top 5 Travl Tipps” in a font that looked like it was designed during an earthquake, with the airplane emoji rendered as a blue smudge.

This wasn’t a one-off. It was every image, every time, across every model we tried.

The problem is fundamental

Current image generation models — Flux, Stable Diffusion, DALL-E, Gemini Imagen — are trained to produce pixels, not glyphs. They’ve seen enough text in training data to approximate what words look like, but they don’t understand typography. They can’t kern. They can’t spell. They definitely can’t render emoji.

Here’s what we saw consistently across hundreds of generated images:

Misspelled words. Not occasionally — regularly. “Restaurant” became “Resturant.” “Schedule” became “Scedule.”
Broken emoji. Emoji rendered as colored blobs, garbled symbols, or just vanished entirely.
Non-Latin scripts destroyed. Arabic text came back as decorative squiggles. Turkish characters like ö, ç, ş were mangled or dropped. This was a dealbreaker — we publish in English, Turkish, and Arabic.
Inconsistent typography. Font weight, size, and spacing changed unpredictably between images in the same batch. No brand consistency possible.

We tried prompt engineering. “Use clean, legible Helvetica.” “Spell all words correctly.” “Render the text at exactly 48px.” None of this works because the model isn’t rendering text — it’s generating an image that resembles text.

The two-step pipeline

The fix was separating image generation from typography entirely. Two distinct stages, two different tools, each doing what it’s good at.

Step 1: AI generates a text-free base image.

The prompt explicitly excludes text: “Generate a vibrant travel photography background. No text, no words, no captions, no overlays.” We use fal.ai with Flux models for this. The AI does what it’s great at — composition, color, mood, visual storytelling.

Step 2: A dedicated tool adds professional typography.

We built image-overlay — a tool that composites text onto images using real font rendering. Real fonts (Poppins for English, Cairo for Arabic headings, Amiri for Arabic body text). Real kerning. Real emoji support. Pixel-perfect positioning.

Pipeline: Text-Free Image Generation → Typography Overlay

prompt → fal.ai/flux → base_image.png → image-overlay → final_image.png
                                              ↑
                                         font: Poppins
                                         size: 48px
                                         position: center
                                         emoji: native render

The difference was night and day. Not a marginal improvement — a category change from “unusable” to “professional.”

Why this matters for multi-language content

We publish across three languages, each with different typographic requirements:

English: Standard Latin characters, straightforward.
Turkish: Requires proper rendering of ö, ü, ç, ş, ğ, ı — characters that AI models frequently corrupt or substitute.
Arabic: Right-to-left text, connected letterforms, diacritics. AI models don’t even attempt to get this right.

With AI-generated text, we’d need to manually QA every single image. With our pipeline, the typography is deterministic. Same font, same size, same positioning rules. If the text is correct in the input, it’s correct in the output.

Implementation details that matter

A few things we learned building this pipeline:

Always request text-free images explicitly. Don’t just omit text from your prompt — actively tell the model not to include any. Otherwise you’ll get phantom text artifacts: half-formed words in backgrounds, watermark-like smudges, decorative text the model thought would look nice.

Font selection is a real design decision. We spent time picking fonts for each language and use case. Poppins for English (clean, modern, reads well at small sizes). Cairo for Arabic headlines (bold, geometric). Amiri for Arabic body text (elegant, traditional). These choices become part of your brand.

Handle emoji as images, not characters. We render emoji using platform-native or custom SVG emoji sets rather than trying to get fonts to handle them. This gives us consistent rendering across every image, regardless of what system generates it.

Template the layouts. We created layout templates for common formats: Instagram square, Instagram story, Facebook post, X/Twitter card. Each template defines safe zones for text, maximum character counts, and fallback font sizes. The overlay tool picks the right template based on the platform target.

templates/
├── instagram-square.json    # 1080x1080, text in lower third
├── instagram-story.json     # 1080x1920, text centered
├── facebook-post.json       # 1200x630, text with padding
└── x-card.json              # 1600x900, minimal text overlay

The broader principle

This is really a separation of concerns argument. AI image generators are good at visual content — composition, color, style, mood. They are bad at precise, deterministic output — exact text, specific layouts, pixel-level control.

The mistake is asking one tool to do both. You wouldn’t ask a photographer to also do the graphic design. You shoot the photo, then a designer adds the text in Figma or Photoshop.

Same logic, automated. Let each tool do what it’s good at.

The takeaway

If you’re building any pipeline that involves AI-generated images with text — social media, marketing materials, presentations, anything — don’t try to get the AI to render the text. It can’t. Not reliably, not across languages, not with brand consistency.

Split the pipeline. Generate the visuals. Overlay the text. It’s more work upfront, but it’s the difference between a system that needs human QA on every output and one that runs autonomously at scale.