For a long time, meaningless letter-like scribble was the clearest giveaway that an image was AI-generated. Posters, packaging and logos were effectively out of reach.
That changed. Ideogram renders text reliably and Flux is close behind. But both work under rules, and ignoring the rules puts you back where you started.
This piece covers why the problem exists, which tools solve it and the three rules that make it work.
Why lettering is hard for these models
Image models learn letters as shapes rather than as writing. They have seen millions of examples of what an "A" looks like, but no rule saying that letters combine into words in a fixed order.
That is why short strings succeed and long ones fail. A three-letter word is close to a single learned shape; a full sentence is a sequence the model has no mechanism to enforce.
It also explains why the same prompt sometimes renders correctly and sometimes does not: like everything else here, it is probabilistic.
The three rules
First: put the text in quotes. Writing "a sign that reads 'NOW OPEN'" makes the tool render the quoted part literally. Without quotes the text is read as part of the prompt description and may not appear at all.
Second: keep it to three words or fewer. One to three words come out correct almost every time; past four the error rate climbs sharply.
Third: verify before use. Generate several variations and pick the correct one. This matters most for brand names, where a single wrong letter makes the image useless.
Which tools actually do this
Ideogram leads clearly and has a generous free tier, which makes it the practical choice for logo and poster work.
Flux is close behind and better when the text sits inside a photorealistic scene — a sign in a street photograph, lettering on packaging shot on a table.
Midjourney has improved but stays behind on longer strings. Stable Diffusion is weak here; text is best added afterwards in a design tool.
For non-English alphabets the picture is worse across the board. Turkish characters illustrate this: ç, ö and ü usually render correctly while ğ, ş and ı often break or turn into their English counterparts.
The professional approach: split the job
Even with the best tool, rendering all of a poster's text inside the image is the wrong workflow.
A concert flyer carries a band name, date, venue, time and price. Render only the headline — one or two words — and reserve empty space in the composition for the rest.
Adding "empty lower third, flat colour" to the prompt guarantees that space. Then add the remaining copy in Canva, Figma or any design tool.
This gives error-free typography and, more usefully, means a date change does not force you to regenerate the image.
Frequently asked questions
Which tool renders text best?
Ideogram, clearly, and it has a generous free tier. Flux is close behind and better when the lettering sits inside a photorealistic scene. Midjourney has improved but stays behind on longer strings.
Why doesn't my text appear at all?
You probably did not put it in quotes. Without quotes the text is read as part of the prompt description rather than as something to render. The second possibility is that it is too long — past three words the error rate rises sharply.
Can I render non-English characters?
Less reliably. Accented and non-Latin characters appear far less often in training data. Turkish is a good example: ç, ö and ü usually work while ğ, ş and ı often break. For critical text, add it afterwards in a design tool.