Nano Banana 2 Text Rendering: How to Add Readable Text to AI Images

For years, asking an AI image generator to put readable text in a picture was basically a dare. You’d get something that looked like a drunk typographer fell asleep on a keyboard. Midjourney V7 got better. Flux got decent. But Nano Banana 2 — Google’s image generator built on Gemini 3.1 Flash Image — treats text rendering as a first-class feature, not an afterthought.

Whether you’re making social media graphics, product mockups, event posters, or meme-grade internet content, this tutorial walks you through exactly how to prompt Nano Banana 2 to produce images with clean, legible, correctly spelled text. No cursed glyphs. No letters that look like they’re having an existential crisis. Just text that actually reads.

What You’ll Achieve

By the end of this tutorial, you’ll know how to write prompts that produce images with accurate, styled text — headlines, labels, slogans, UI elements, signage, and more. You’ll also learn which prompt structures consistently work, which approaches trip the model up, and how to use the editing workflow to fix text that came out slightly off. Access is available through the Gemini app, AI Studio, the Gemini API, and Vertex AI.

Requirements

You need access to Nano Banana 2 through one of its available surfaces. The Gemini app (gemini.google.com) is the fastest entry point for casual use — no setup required, just start prompting. For API access, you’ll need a Google account with Gemini API enabled through Google AI Studio (aistudio.google.com), or a Google Cloud project for Vertex AI. Developers building apps or needing batch generation should go the API route. All generated images carry a SynthID watermark — Google’s invisible digital watermark baked into every output at the pixel level. You can’t see it, but it’s there, and it survives cropping and screenshot exports.

How Text Rendering Works in Nano Banana 2

The model has been trained to understand that text in images is a visual element with semantic meaning — it needs to match what the prompt specifies, letter for letter. Earlier image generators treated text as just another texture pattern, which is why they produced plausible-looking-but-wrong gibberish. Nano Banana 2 actually attempts to render the string you specify, and for short-to-medium text strings it does so with high accuracy.

The practical limits: single words and short phrases (under ~30 characters) come out reliably. Longer text blocks get shakier. If you need a paragraph in the image, you’re better off compositing in Canva or Figma afterwards and using the AI for the visual background. But for headlines, product names, slogans, labels, and UI copy? Nail it in the prompt and Nano Banana 2 will deliver.

Pro tip ✅

Always put the exact text you want rendered in quotation marks inside your prompt. “A poster with the text ‘Launch Day'” works significantly better than describing the text without quotes. The model treats quoted strings as literal targets, not paraphrases.

The Core Prompt Structure for Text in Images

The formula that consistently works is: [image type] + [visual style/mood] + [text placement] + [exact quoted text] + [typographic style] + [resolution/quality]. You don’t need all six elements every time, but the more specific you get, the less you’ll be re-rolling outputs.

Here’s the base template, then a set of ready-to-use prompts across different use cases.

A [image type], [visual style], with the text "[YOUR TEXT HERE]" displayed prominently in [font style] typography, [placement], [lighting/color], 4K resolution

That’s your skeleton. Now let’s put meat on it.

Ready-to-Use Prompts

Social media announcement graphic:

A bold social media graphic, dark navy background with electric blue gradient accents, centered text reading "IT'S LIVE" in large condensed sans-serif uppercase letters, subtle geometric lines in the background, clean and modern, 4K resolution

This works because the short two-word string gives the model minimal room to introduce errors, the high-contrast background makes legibility easy, and specifying “condensed sans-serif uppercase” locks in the typographic style so the letters don’t drift into decorative territory that gets harder to read.

Product label mockup:

A close-up product shot of a dark glass bottle with a premium label, matte black label with gold foil text reading "OBSIDIAN" at the top and "Small Batch" in smaller elegant serif type below, studio lighting on white background, photorealistic, 4K

Product labels are where Nano Banana 2 earns its keep. The two separate text elements at different sizes test hierarchy rendering — and specifying “gold foil” gives the model a visual treatment that naturally renders crisply because of the contrast with the dark label.

Event poster — portrait format:

A vertical event poster, retro 1970s concert aesthetic, warm orange and brown tones, torn paper texture background, large bold title text reading "SONIC WAVE" at the top, smaller subtitle text "May 15 – Festival Grounds" below, psychedelic border details, film grain effect

Retro aesthetics work particularly well for text rendering because they favor bold, high-contrast type that the model handles confidently. Asking for “large bold title” vs “smaller subtitle” also signals the visual hierarchy explicitly, which helps.

Minimalist quote card:

A minimalist quote card, off-white linen texture background, centered handwritten-style script text reading "Start before you're ready" in dark charcoal ink, generous white space, soft drop shadow under the text, no other elements, clean and editorial

Script fonts are trickier than block type, but short phrases like this stay within reliable territory. “No other elements” is doing real work here — it removes visual competition that might confuse the model’s composition logic.

UI/app screenshot mockup:

A photorealistic smartphone screen mockup showing a dark mode app interface, status bar visible at top, a prominent button with the label "Get Started" in white medium-weight sans-serif, clean card layout below with a heading "Your Dashboard" and a subtitle "Everything in one place", soft blue accent color, floating device on neutral background

Multiple text elements in one image is the real stress test. This prompt passes by keeping each string short and semantically distinct. Specifying font weight (“medium-weight”) helps the model avoid defaulting to either ultra-thin or heavy black type.

Storefront/signage:

A street-level photo of a modern café storefront, warm evening light, illuminated sign above the entrance reading "GROUNDS" in backlit channel letters, wooden facade with plants framing the entrance, shallow depth of field, cinematic color grade

Environmental signage is where Nano Banana 2 really shows off — integrating text into a physical scene with realistic lighting on the letterforms. “Backlit channel letters” is a specific enough visual instruction that the model renders them with the glow and edge lighting you’d expect from real signage.

Meme-style image with caption text:

A photorealistic image of a very tired-looking golden retriever slumped over a laptop keyboard, impact font style text at the bottom reading "MONDAY ENERGY" in white with a thin black outline, bold and centered, high contrast, slightly humorous mood

Classic meme format: white Impact-style text with an outline at the bottom of the image. This is a case where being explicit about “white with a thin black outline” matters — it’s the exact treatment that makes meme text readable against any background.

Book or album cover:

A dramatic book cover design, vertical format, dark foggy forest at dawn with a single figure walking away on a path, large title text "THE LONG WAY HOME" at the top in serif font with slight tracking, author name "A. CALDWELL" in smaller caps at the bottom, moody atmospheric lighting, cinematic

Book covers require the model to balance illustration and typography. Specifying “slight tracking” (letter spacing) is a typographic detail that pushes the output toward more professional-looking results. “Smaller caps” at the bottom creates a natural hierarchy without needing to spell out relative sizes numerically.

Pro tip ✅

Keep text strings under 25 characters per element for best accuracy. If you need longer copy, split it across multiple labeled elements in your prompt: “a heading reading ‘X’, a subheading reading ‘Y’, a caption reading ‘Z'”. This gives the model clear structure to work with instead of one long string it might truncate or misspell.

Warning ⚠️

Avoid asking for text in highly decorative, complex backgrounds at small sizes. If your prompt has a busy illustrated scene and a tiny disclaimer-style text block at the bottom, the model will either skip the small text or render it as visual noise. Save fine print for post-production in a design tool.

Pro tip ✅

If your first output has a misspelled word, use the edit/regenerate function with a targeted instruction: “Keep everything the same, but fix the text to correctly read ‘[YOUR CORRECT TEXT]'”. The iterative editing workflow in the Gemini app and AI Studio accepts natural-language edit instructions, so you don’t have to rebuild the entire prompt from scratch.

Note 💡

SynthID watermarks are embedded in every Nano Banana 2 output — including outputs accessed via the Gemini API, AI Studio, and Vertex AI. The watermark is imperceptible to the human eye and persists through common edits like cropping, color adjustments, and format conversion. If you’re using outputs in commercial contexts, this is worth knowing — Google can verify the AI origin of any image through the watermark layer.

Avoid 🚫

Don’t mix too many font style instructions in a single prompt. Asking for “bold serif italic condensed handwritten” in the same breath gives the model conflicting signals and produces unpredictable results. Pick one or two typographic descriptors and be consistent.

Using the Editing Workflow for Text Fixes

Even well-constructed prompts occasionally produce a slightly wrong character — an extra letter, a wrong case, a missing word. The editing workflow in both the Gemini app and AI Studio handles this well with conversational follow-up prompts. After your initial generation, you can type something like: “Same image, but change the text to read ‘LAUNCH DAY’ instead of ‘LAUNCK DAY'”. The model preserves the composition, lighting, and style while targeting the correction.

For Gemini API and Vertex AI users, this means sending a follow-up request with the prior output image as context plus the correction instruction. The image-plus-text multimodal input handles this exactly the way a human art director might brief a revision — which is a much better workflow than re-prompting from zero every time a single character is wrong.

Nano Banana vs. Nano Banana Pro on Text

Nano Banana Pro produces sharper results on complex multi-element compositions and handles longer text strings with higher accuracy. If you’re generating professional print materials or need text blocks longer than a headline, the Pro tier is worth the step up. For social graphics, quick mockups, and anything under 30 characters per text element, standard Nano Banana 2 handles the job without needing the upgrade.

What This Means for Your Workflow

Readable text in AI-generated images was the last major usability hole in this category, and Nano Banana 2 patches it to a practically useful level. You’re not replacing a dedicated designer or Canva for complex multi-page layouts — that’s not the point. But for rapid social content, mockup visualization, signage concepts, or anything where you need a visual plus a readable word or phrase in minutes rather than hours, the workflow is now genuinely faster than the alternative. Get the prompt structure right, keep your text strings short and quoted, and you’ll spend more time using the output than fixing it.

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