Text Rendering in Z-Image Base: Getting Readable Typography

Text rendering has historically been one of AI image generation's biggest weaknesses. Words come out garbled, letters get scrambled, and signage becomes illegible nonsense. Z-Image Base represents significant improvement in this area, though it's still not perfect. Understanding what works and what doesn't helps you get the best possible text results.

Text rendering capability has improved dramatically in recent model generations, and Z-Image Base is among the better performers in this challenging area.

Understanding Text Rendering

Why is text so hard for AI models, and how does Z-Image Base approach it?

Why Text is Difficult

Traditional image generation models struggle with text because:

Character-level precision: Text requires exact letter shapes. A slightly distorted "A" is still recognizable as a face, but becomes unreadable as a letter.

Sequential information: Words are sequences where order matters. "STOP" and "POTS" use the same letters but mean different things.

Contextual rendering: The same word should look different on a neon sign versus a book page.

Scale challenges: Small text requires precise detail that conflicts with how diffusion models generate images.

Z-Image Base's Approach

Z-Image Base's S3-DiT architecture provides advantages:

Better detail preservation: The sliding window attention helps maintain sharp detail at all scales.

Improved text encoding: The text encoder better connects written prompts to visual text rendering.

Consistency: Results are more predictable, allowing for iteration and refinement.

These improvements make Z-Image Base one of the more capable models for text, though challenges remain.

What Works Well

Let's start with scenarios where Z-Image Base excels.

Short Words and Phrases

Single words and very short phrases have the highest success rate:

Excellent:

• "OPEN" / "CLOSED"
• "STOP"
• "HELLO"
• "SALE"
• Brand names (1-2 words)

Good:

• "Coffee Shop"
• "Welcome Home"
• "No Entry"

Challenging:

• Full sentences
• Complex multi-word phrases
• Small disclaimer text

Signs and Labels

Contextual signage renders well:

Prompt: "A coffee shop storefront with a sign reading 'BREW' above the entrance, urban street scene, morning light"

The model understands that signs should have certain visual properties.

Stylized Text

Text integrated into artistic contexts:

Prompt: "Neon sign reading 'JAZZ' glowing in a dark alley, cyberpunk atmosphere, rain reflections"

Stylized contexts often produce better results than plain text.

Short, contextual text renders most reliably

Prompting Techniques

Specific prompting strategies improve text rendering success.

Be Explicit About Text

Always use quotes and clear instructions:

Good:

"A wooden sign with the text 'FARM FRESH' painted in red letters"

Better:

"A wooden sign with the text 'FARM FRESH' painted in large red capital letters, rustic style, barn background"

Poor:

"A farm fresh sign"

Specify Location and Size

Tell the model where and how text should appear:

"Large bold text 'SALE' in the center of the image, retail poster style"

"Small label reading 'organic' in the corner of a product photo"

"Banner across the top reading 'WELCOME'"

Describe Typography Style

Include stylistic details:

"Text 'COFFEE' in elegant serif font, gold letters on dark background"

"Graffiti-style text 'DREAM' spray painted on brick wall"

"Minimalist sans-serif text 'hello' in white on pastel pink"

Use Context

Place text in natural contexts:

"Book cover with title 'MYSTERY' in dramatic font"

"Movie poster with 'COMING SOON' text at bottom"

"T-shirt with 'MUSIC' printed on front"

Common Issues and Solutions

Understanding typical failures helps you avoid them.

Scrambled Letters

Problem: Text appears but letters are wrong or scrambled.

Solutions:

• Use shorter text
• Increase emphasis: "clearly readable text 'WORD'"
• Try multiple seeds
• Add "legible" or "readable" to prompt

Missing Characters

Problem: Some letters don't appear.

Solutions:

• Reduce word length
• Ensure adequate space for text
• Use larger text descriptions
• Regenerate with different seeds

Distorted Shapes

Problem: Letters are warped or unrecognizable.

Solutions:

• Lower CFG (try 5-6)
• Specify "clean typography"
• Use fonts known to work well
• Add negative prompt: "distorted text, garbled letters"

Wrong Text Appearing

Problem: Different text appears than what was prompted.

Solutions:

• Put exact text in quotes
• Repeat the text in prompt
• Be very explicit about what text should appear
• Remove conflicting words from prompt

Limitations to Accept

Some text scenarios remain difficult regardless of technique.

Long Text

Sentences and paragraphs rarely render correctly. If you need substantial text, plan on post-processing.

Small Text

Fine print, disclaimers, and small labels are unreliable. The resolution constraints make tiny text inconsistent.

Multiple Text Elements

Multiple different text elements in one image increase failure probability. Each additional word compounds difficulty.

Specific Fonts

Requesting exact fonts rarely works as expected. The model interprets style suggestions but doesn't have font libraries.

Understanding limitations helps set realistic expectations

Hybrid Approaches

For professional needs, combining AI generation with traditional tools often produces best results.

Generate Then Add Text

1. Generate image with placeholder or no text
2. Export to design software (Photoshop, Figma, etc.)
3. Add text using proper typography tools
4. Match style to the generated image

This approach gives you:

• Perfect text every time
• Full font selection
• Complete control over positioning
• Professional typography

Inpainting Text Regions

1. Generate image with approximate text placement
2. Use inpainting to refine specific text areas
3. Iterate until satisfactory

This works for single words that almost rendered correctly.

ControlNet for Text Placement

Advanced workflows can use ControlNet:

1. Create text layout image
2. Use as control input
3. Generate with text guidance

Success varies but can improve placement consistency.

Comparison with Alternatives

How does Z-Image Base compare for text rendering?

Z-Image Base is competitive but not best-in-class for text. Midjourney currently leads, though their approach is proprietary.

Use Cases

Where does Z-Image Base text rendering work well in practice?

Marketing Mockups

Quick concept generation where text doesn't need to be perfect:

• Social media post concepts
• Advertisement rough drafts
• Packaging exploration

Artistic Pieces

Text as visual element rather than information:

• Graffiti and street art
• Neon signs and displays
• Stylized posters

Signage and Labels

Simple contextual text:

• Store signs
• Warning labels
• Simple notices

NOT Recommended For

• Legal documents with fine print
• Technical diagrams with annotations
• Book covers requiring exact titles
• Anything requiring precise long text

Frequently Asked Questions

Can Z-Image Base render any text perfectly?

No AI model is 100% reliable for text. Short words have high success rates, but perfection isn't guaranteed.

How do I get exact fonts?

You can't specify exact fonts. Describe the style (serif, sans-serif, bold, elegant) and the model interprets.

Why does longer text fail?

Sequential character accuracy compounds. Each additional letter increases chance of errors.

Should I use all caps or mixed case?

ALL CAPS is slightly more reliable due to simpler letterforms.

Can I render text in other languages?

Results vary by language. Latin alphabets work best. CJK characters are less reliable.

How many words can I expect to work?

1-3 words is the sweet spot. 4-5 sometimes works. Beyond that, expect issues.

Does higher resolution help text quality?

Somewhat. Higher resolution allows more detail, but the fundamental challenges remain.

Why does the same prompt give different text results?

Text rendering is one of the most variable aspects. Different seeds produce different results.

Can LoRAs improve text rendering?

Some LoRAs focus on typography improvement, with varying effectiveness.

What's the best negative prompt for text?

Try: "distorted text, garbled letters, illegible, scrambled words, misspelled"

Text rendering in Z-Image Base represents the current state of AI image generation: improved but imperfect. For many creative applications, the current capabilities are sufficient. For professional work requiring exact text, plan on using hybrid workflows that combine AI's strengths with traditional design tools.

For users wanting to experiment with text generation alongside other AI capabilities, Apatero offers Z-Image Base among 50+ models with features including video generation and LoRA training on Pro plans.