AI Infographic Generator for Product Listings: Skip the Designer

Infographics do half the selling on a product page. Specs, benefits, dimensions, a shopper picks all that up in a couple of seconds without reading the description. A designer used to make these for you, and it wasn't cheap or fast. I handle visual content at Flami, and in this guide I'll walk through how to build a decent infographic and get clean text on a product image using an AI infographic generator, no layout software, no garbled letters pretending to be a sentence.

One thing upfront: infographics are exactly where most image models fall apart. Text inside a generated image has always been their weak spot. So first I'll cover which models are actually worth your time, then how to talk to them so the fonts don't fall apart.

Why a regular image generator won't cut it

Open any general-purpose image generator and ask for "an infographic with product specs," and you'll get a pretty background and a smear of nonsense where the labels should be. For most models, text isn't text, it's a pattern that resembles letters. That's fine for a face or a fabric texture, but fatal for an infographic, where the entire point is that people can read it. Fatal for an infographic, where the entire point is that people can read it.

So you need models from a narrow group that were specifically trained on typography. There aren't many, and here they are.

Three models you can actually trust with text

Ideogram 3.0 for typography is my first pick whenever the image exists to carry text. It renders labels more accurately than anything else on the market. On top of that, its Layerize feature turns text into an editable layer, so you can fix a caption or swap a number without regenerating the whole graphic. For infographics, where a client inevitably asks you to "change 30% to 40%" after approval, that's a lifesaver. I went deeper on this in my Ideogram 3.0 review.

Nano Banana from Google is where I go when I need accurate text and a well-thought-out layout around it. It runs on a reasoning engine, so it actually understands spatial relationships and places captions exactly where they belong instead of wherever happens to fit. Icons, callout lines, a product diagram with labeled parts: that's its strong suit. Check the Nano Banana review for examples.

Flux 2 for product scenes comes in when the infographic sits on top of a photorealistic product shot and brand color matters. Flux reproduces a HEX code pixel for pixel, so backgrounds and badges line up with your brand guide exactly. It handles text cleanly too, though on dense, complicated labels I trust it a bit less than Ideogram, it really depends on how the scene is composed. The Flux 2 breakdown has more detail with samples.

That's the core trio. If your infographic carries a heavy load of text, Qwen Image 2.0 is worth adding to the mix, it holds up well on dense text blocks, something I covered in a separate piece on text accuracy in image models.

How to write a prompt for an infographic

The logic here differs from a normal generation prompt. The main rule: whatever text needs to appear, write it out word for word, in quotes. Not "a caption about water resistance," but exactly "text reading: Waterproof to 50 meters." The model will render precisely what's inside the quotes.

Next, describe the layout, what sits where. Typography-focused models understand spatial instructions: "headline top left, product centered, three benefit icons on the right with captions underneath." The clearer the layout, the less the model improvises on its own.

Here's a template I reuse often:

"Product listing infographic, 3:4 format, clean light background. [Product] centered and large. Headline at the top reading: '[Product name]'. Three icons on the left with short captions: '[benefit 1]', '[benefit 2]', '[benefit 3]'. A band at the bottom reading: '[key spec]'. Neat grid layout, readable sans-serif font, brand background color #[HEX]. Clean, minimalist style, like a premium Amazon listing."

Swap in your own values. The more specific the quoted text and the simpler the layout, the cleaner the result comes out.

Common mistakes that bring the garbled text back

The most frequent one, from what I've seen, is cramming too much text into one frame. The more labels you ask for, the higher the odds that something slips. If you've got a long list of specs, split it across two or three gallery images instead of forcing everything onto one. It's easier on shoppers too.

Next up: small body text in a paragraph. Almost no model holds up long text at a tiny point size. If you genuinely need a full ingredient list or instructions, either reach for Wan 2.7 Image, which handles dense typography well and outputs native 4K (there's a full review), or keep the long text on a plain overlay and let the AI handle the visuals and the large headline text only.

And one more trap: text that isn't quoted. Write "a caption about the warranty" instead of "text reading: 2-Year Warranty," and the model invents its own wording, often with a typo baked in. Dictate it exactly.

Last thing, and the one that matters most: check every finished infographic with your own eyes before uploading it. Even a strong model will occasionally flip a letter or a digit, and on a product image that reads as a defect. Catching a typo before publishing costs a lot less than fielding customer questions afterward.

Putting it together in Flami

All these models live under one Flami subscription. My usual workflow: generate the base scene with the product and background on Flux 2 or Nano Banana, then if the text is heavy or complex, build the final version with the captions on Ideogram 3.0 specifically for Layerize, so fixing a number later stays easy.

If you'd rather skip prompt-writing and just need a batch of similar listings, the product-to-video templates mode already has the layout baked in, you just drop in your own product. But for a one-off infographic on a hero product, building it manually gives you more control. Start with a simple layout and short captions, then add complexity later, it's cheaper than starting with an overloaded frame and wondering why the text came out as mush.

Build an infographic for your own listing → flami.pro

FAQ

Which AI model is best for infographics and text on images? Ideogram 3.0 (90-95% text accuracy plus editable Layerize layers), Nano Banana (accurate text with a well-planned layout), and Flux 2 (when brand color matters most). For long or non-Latin text blocks, add Qwen Image 2.0 or Wan 2.7 Image.

How do I get an AI model to render exact text on an image? Write the exact text in your prompt, word for word, in quotes. Not "a caption about the warranty," but "text reading: 2-Year Warranty." The model renders exactly what's inside the quotes. Also specify the layout, where each element sits.

Why does AI produce gibberish instead of real letters? For most models, text is a visual pattern that resembles letters rather than actual text. That's why infographics need models specifically trained on typography: Ideogram 3.0, Nano Banana, Flux 2, and Qwen Image 2.0 for non-Latin scripts.

Can I fix a number on a finished infographic? Yes. Ideogram 3.0's Layerize feature turns text into an editable layer, so you can change a word or number without regenerating the whole image. Handy when a client asks you to change 30% to 40% after the fact.

How many captions should fit on one image? Fewer is cleaner, and easier to read at a glance. If you have a lot of specs, split them across two or three gallery images rather than overloading one. It's easier for shoppers to skim too.

Sources

1. Flami: Ideogram 3.0 review
2. Flami: Nano Banana review
3. Flami: Flux 2 review
4. Flami: text accuracy in AI image generators