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 from pixels, so it spells the way it draws fur or foliage.
Fastest fix (works for ~80% of cases): move just the text-bearing image to a typography-grade model and put your exact copy in straight quotes. As of June 2026 the three strongest options are Ideogram 3.0 (best for posters with multiple text blocks), GPT Image 2 (best for non-Latin scripts and dense layouts; the default image model in ChatGPT since April 21, 2026), and Nano Banana 2 / Gemini 3.1 Flash Image (fastest, and it can fix the wrong word with one plain-language instruction, no mask). If the string still breaks after a model swap, stop re-rolling and composite real type in Figma or Photoshop. The rest of this guide is the decision path for when the fast fix is not enough.
Which bucket are you in
Diagnose before you act. Most garbled-text cases are one of these:
| Symptom | Most likely cause | Go to |
|---|---|---|
| Every letter is wrong, any prompt, any seed | Weak-text model (SDXL / SD 1.5 / old Midjourney) | Step 1 |
| First few letters fine, then it drifts | String too long (over ~20 chars) | Step 2 |
| Text fine on big banners, mush on small labels | Text area too few pixels | Step 3 |
| Block text works, your “script font” does not | Stylized font request | causes #4 |
| One of three text blocks is always wrong | Multiple strings competing | Step 1 (Ideogram 3.0) |
| Latin fine, CJK / Arabic / accents garbled | Non-Latin script on a weak-text model | Step 1 (GPT Image 2) |
| Only 1 of 4 candidates breaks | Seed noise, not structural | re-roll the seed |
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 Midjourney (through v7, and still mostly true on the v8.1 default that shipped June 10, 2026) treat letters as visual noise patterns. Even with perfect prompting they rarely produce more than 4-6 correct characters in a row, and independent 2026 tests still peg Midjourney’s in-image text accuracy around 30-40% on multi-word strings. The typography-grade models below were trained with text-aware objectives and clear ~90-95% on short strings:
- Ideogram 3.0 sits at roughly 90-95% on headline text and is the only model that reliably places several separate text blocks in one layout.
- GPT Image 2 (ChatGPT default since April 21, 2026; also
gpt-image-2via the OpenAI API) reports ~95-99% on Latin and is the first mainstream model to render CJK, Hindi, Bengali, and Arabic at production quality. - Nano Banana 2 / Gemini 3.1 Flash Image matches them on short text and renders in 1-2 seconds.
- Flux (Pro / Dev / Flux 2) and Imagen 4 / Imagen 4 Ultra handle single-line headlines well but are weaker on multi-block layouts.
How to spot it: Look at your model. If it is SDXL / SD 1.5 / 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 20-25 characters in a single string. A “SUMMER SALE 2026” banner is reliable; a full paragraph of marketing copy is not. Splitting one long line into two shorter quoted lines usually recovers accuracy.
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 below that floor and turns to mush.
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, and 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% fall sharply across three; Ideogram 3.0 is the most resilient here because it allocates a layout pass per block.
6. Non-Latin scripts
Older models were trained primarily on English, so CJK, Arabic, Cyrillic, Devanagari, and even accented Latin (German umlauts, French accents) broke far earlier than plain ASCII. This is the one cause that materially changed in 2026: GPT Image 2 and Nano Banana 2 now render these scripts well, so for non-Latin text the fix is usually “use one of those two models,” not “give up on AI text.” Older or open-weight stacks (SDXL, Flux without a CJK fine-tune) still mangle non-Latin scripts.
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 Midjourney, the single biggest move is hopping to a model with text-aware training. As of June 2026, pick by job:
- Ideogram 3.0: strongest at short headline text, multiple separate text blocks, and basic typographic styling. Default here for posters, ads, and signage with more than one line of copy.
- GPT Image 2 (in ChatGPT, or
gpt-image-2via API): best for non-Latin scripts (Chinese, Japanese, Korean, Hindi, Bengali, Arabic) and for dense or curved-surface layouts. - Nano Banana 2 / Gemini 3.1 Flash Image (in the Gemini app or via the Gemini API): fastest, strong on short text, and it can edit the text in an existing image from a plain instruction.
- Flux 2 (Pro / Dev) and Imagen 4 / Imagen 4 Ultra: very strong photorealism and single-line headlines; weaker on multi-block layouts.
Generate the text-heavy regions in one of these, even if your main pipeline stays on SDXL or Midjourney. A common pro split is base image in Flux or Midjourney, text block regenerated in Ideogram 3.0 or composited (Step 5).
Step 2: Quote your exact words, then shorten and simplify
Two moves here, in order:
- Put the exact copy in straight quotes inside the prompt, for example: the text reads
"SUMMER SALE". Quoting is the single highest-leverage prompt change on GPT Image 2, Ideogram 3.0, and Nano Banana 2, and it reliably pushes Latin accuracy toward 99%. On GPT Image 2 you can also end the instruction withspell it verbatim, no extra charactersfor unusual brand names. - Then trim. Cut each quoted line to ~20 characters or fewer, drop optional punctuation, and prefer uppercase (uppercase glyphs are easier for models than lowercase). Spell an unusual brand name letter by letter the first time. You can restore accents and special characters in the compositing step if a weaker model drops them.
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: Fix just the text region (no full re-roll)
If composition is locked but the text is wrong, do not regenerate the whole image. The fastest path in 2026 is conversational, maskless editing:
- Nano Banana 2 / Gemini app: keep the image in the chat and say
change the headline to read "SUMMER SALE", keep everything else identical. Its semantic segmentation finds the text region with no mask. - GPT Image 2 in ChatGPT: reply in the same conversation,
fix the spelling so the title reads "SUMMER SALE" exactly. It re-renders the existing image rather than starting over.
If you are on an open-weight or manual stack, mask explicitly instead:
- SDXL / A1111: img2img inpaint on the text region, denoising 0.6-0.8, prompt focused only on the quoted text.
- ComfyUI: an inpaint workflow with a manual rectangular mask covering the text.
- Midjourney: Editor / Vary (Region), brush the text area, rewrite the prompt to the quoted text content.
- Photoshop: Generative Fill on the text area, prompted with just the desired quoted 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
- For print and any project where one wrong letter is unacceptable, composite real type. Reserve in-image AI text for moodboards, concepts, and quick social cards.
- Maintain a model preference list: multi-block text goes to Ideogram 3.0; non-Latin text goes to GPT Image 2 or Nano Banana 2; photoreal base plates can stay on Flux 2 / Midjourney with the text added later.
- Always quote exact copy and 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 on weak-text or open-weight stacks (SDXL, vanilla Flux), still composite type from the start; on GPT Image 2 / Nano Banana 2 you can let the model render the script, then verify every character with a native reader.
Common pitfalls
- Re-rolling the same prompt 20 times waiting for a good text generation. After 4 bad rolls, switch model or composite.
- Not quoting the copy. Unquoted text invites the model to paraphrase or “improve” your words.
- 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: As of June 2026, Ideogram 3.0 handles multi-block layouts best (a headline, subhead, and tagline in one shot) at roughly 90-95% accuracy. Flux 2 and Imagen 4 are strong on a single line but weaker when three or more blocks stack. GPT Image 2 is the pick when any of those blocks is non-Latin.
Q: What is the single fastest prompt fix?
A: Put the exact copy in straight quotes, for example "SUMMER SALE". On GPT Image 2, Ideogram 3.0, and Nano Banana 2 this alone pushes Latin text toward 99%. Add spell it verbatim, no extra characters for odd brand names.
Q: Can I fix the wrong word without regenerating the whole image? A: Yes. In the Gemini app (Nano Banana 2) or ChatGPT (GPT Image 2), reply in the same conversation with the corrected quoted text and “keep everything else identical.” No mask is needed; the model edits the text region in place. On SDXL / ComfyUI / Midjourney, inpaint the region manually (Step 4).
Q: Is AI finally usable for Chinese, Japanese, or Arabic text? A: For short strings, yes, since GPT Image 2 (April 2026) and Nano Banana 2 render CJK, Hindi, Bengali, and Arabic at production quality. Still verify every glyph with a native reader, and for long or legally sensitive copy, composite real type. Older and open-weight models continue to mangle non-Latin scripts.
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. Switching model or quoting the copy gives the real gains.