You generate a poster, a product mockup, or a UI screen. The composition lands, the lighting lands — but the headline reads “DSCONUT” or “Sumemr Sael”. For most diffusion models, text is not a first-class primitive; letters are textures the model approximates. The fix is rarely “prompt harder.” It is choosing the right model for the text bits, masking the text region, and falling back to compositing real type when the run-cost of regeneration starts to exceed five minutes.
Common causes
Ordered by how often each is the actual root cause.
1. The model has weak text rendering by design
SD 1.5, SDXL base, and older Midjourney versions treat letters as visual noise patterns. Even with perfect prompting, they rarely produce more than 4-6 correct characters in a row. Flux.1, Ideogram 2, Imagen 3, and DALL-E 3 were trained with text-aware objectives and handle short strings much better.
How to spot it: Look at your model. If it is SDXL / SD 1.5 / older Midjourney, the model is the cause. Switching models is faster than fighting it.
2. Text string is too long
Even text-aware models degrade past 15-20 characters in a single string. A “SUMMER SALE 2026” banner often works; a full paragraph of marketing copy does not.
3. The text area is too small in the canvas
The same pixel-budget rule as faces. Letters need roughly 32-48 pixels of vertical resolution per character. A small badge or footer line gets crushed.
4. Stylized fonts are being requested
“Handwritten cursive on parchment”, “graffiti spray-paint letters” — these push the model into low-data territory. Even text-aware models default to block-sans behavior; deviating from it produces malformed glyphs.
5. Multiple text strings in one image
A poster with a headline, a subhead, and a tagline asks the model to render three independent text regions correctly. Models that handle one string at 95% accuracy fall to 30% across three.
6. Non-Latin scripts
Most models were trained primarily on English. CJK, Arabic, Cyrillic, Devanagari, and even accented Latin (German umlauts, French accents) break much earlier than plain ASCII.
Before you start
- Save the seed, full prompt, model, and tier of the broken generation.
- Decide whether the text must be AI-generated at all. For production design work, compositing real type in Figma / Photoshop is almost always faster and pixel-perfect.
- Count the characters and distinct text strings in your prompt. Note which strings are critical and which are decorative.
- Generate 4 candidates at the same prompt and different seeds. If only 1 of 4 has bad text, it is seed noise; if all 4 break, the prompt or model is structural.
Information to collect
- Full prompt, model name, version, and tier.
- A 100% crop of the garbled text region.
- Whether other generations from the same model also break text the same way.
- The intended use (print, web hero, social card) — print needs the most accuracy.
- Total characters and number of distinct text strings requested.
Step-by-step fix
Ordered by ROI.
Step 1: Switch to a text-aware model for the run
If you are on SDXL or older Midjourney, the single biggest move is hopping to a model with text-aware training:
- Ideogram 2: currently the strongest at short headline text, multiple strings, and basic typographic styling.
- Flux.1 (Pro / Dev): very strong on single-line headlines; weaker on multi-string layouts.
- DALL-E 3 (via ChatGPT or API): solid on 10-20 character strings.
- Imagen 3: strong on signage, badges, and labels.
Generate the text-heavy regions in one of these, even if your main pipeline stays on SDXL or Midjourney.
Step 2: Shorten and simplify the text
Cut the headline to 15 characters or fewer. Drop punctuation where you can. Force uppercase — uppercase glyphs are easier for models than lowercase. Remove accents and special characters; you can add them back in the compositing step.
Step 3: Promote the text region in the canvas
If the text must be in-shot, change the framing so the text area fills more pixels:
- For a poster, switch to a tall aspect ratio so the headline gets more vertical pixels.
- For a product mockup, zoom in on the label.
- For a UI screen, generate the screen at higher base resolution.
Step 4: Mask and inpaint the text region
If composition is locked but the text is wrong, do not regenerate the whole image:
- SDXL / A1111: Use img2img inpaint on the text region, with denoising 0.6-0.8 and a prompt focused only on the text.
- ComfyUI: Use an inpaint workflow with a manual rectangular mask covering the text.
- Midjourney: Use Vary (Region), brush the text area, and rewrite the prompt to focus on the text content.
- Photoshop: Use Generative Fill on the text area, prompted with just the desired string.
Step 5: Composite real type as the final fallback
If the text still breaks after steps 1-4, stop fighting the model. Generate the image without text (or with a placeholder rectangle), then add real type in Figma / Photoshop / Affinity. This is the standard production workflow for any design work where text accuracy matters more than 30 seconds of additional time.
Steps for the clean handoff:
- In the AI prompt, replace text strings with “blank rectangular label” or “empty banner”.
- Generate the image with the label area visible.
- Open in your design tool, place a text layer over the label.
- Match perspective using transform / warp if the label is on a 3D surface.
How to confirm the fix
- Read every character of every text region at 100% zoom. Misspellings and dropped letters are easy to miss at fit-to-screen.
- Have someone else read the text aloud. Self-blindness on your own copy is a real failure mode.
- For multi-string layouts, verify each string independently.
- Print at the intended final size if it is a print piece — letters that look fine on screen can fall apart in print.
Long-term prevention
- Default to compositing real type for any project where text accuracy matters. Reserve in-image AI text for moodboards and concepts.
- Maintain a model preference list: text-heavy work goes to Ideogram or Flux; text-light work can stay on Midjourney or SDXL.
- Keep headlines short and uppercase by default.
- Build a “blank label” prompt template that intentionally avoids text generation so you can composite later.
- For multilingual work, never trust the model on non-Latin scripts — composite type from the start.
Common pitfalls
- Re-rolling the same prompt 20 times waiting for a good text generation. After 4 bad rolls, switch model or composite.
- Trusting a screenshot read at small size; always zoom to 100%.
- Forgetting that auto-correct in your prompt may have changed words before you submitted.
- Adding “high quality typography” to the prompt and expecting it to fix a structurally weak text model.
FAQ
Q: Which model is best for posters with multiple text strings? A: Ideogram 2 currently handles multi-string layouts best. Flux.1 is close on single-line headlines but weaker when three or more strings are stacked.
Q: Can I force the model to use a specific font? A: Not reliably. You can describe style (“bold sans-serif”, “serif headline”) and get an approximation, but exact font matching only happens via compositing.
Q: Why does the text break worse at small sizes? A: Pixel budget. Each character needs roughly 32-48 vertical pixels to render legibly. Small text falls below that floor.
Q: Is there a negative prompt that fixes garbled text?
A: Adding garbled text, misspellings, malformed letters to the negative prompt helps slightly on SDXL but does not solve the underlying training gap. Switch model for real gains.