Adding clear, well-placed text to AI-generated images was a frustratingly common failure point relatively recently. Otherwise impressive visuals often marred by gibberish phrases and distorted letters. Fortunately, the most recent generation of image models is slowly beginning to address this challenge, as a number of platforms have demonstrated (subtle) ability to incorporate text in a coherent manner. Why is this so difficult? It forces the AI to deal with semantic meaning (what the words mean), stylistic consistency (what the text looks like) and spatial reasoning (where the text needs to go in the image composition) all at the same time. The challenge of balancing these elements together is not simple. Multiple recent models are known for their improved text capabilities. Ideogram has received early praise for its emphasis on accurate text layout, with even tricky prompts often leading to impressive outputs. Another major feature touted in OpenAI’s latest 4o model is significantly better rendering of text, which the company credited to its more integrated approach to processing language and visual data. Other top platforms, such as Midjourney and DALL-E 3 (inside ChatGPT Plus) have improved as well, though consistency can still waver based on a prompt’s complexity and the desired style. Reliably generating text gives rise to a host of creative capabilities: creating meme variations on the fly, embedding slogans in posters, adding onto figures as labels or rich diagrams in teaching tools, designing merchandise prototypes with on-demand mockups, overlaying captions on scene elements, and more. So while perfection isn’t a reality in all models yet, the speed at which things are improving means that being able to produce legible, contextually appropriate text in an AI image is no longer something we look upon as a rare exception but rather something that makes more sense to get used to as a standard feature in AI image generation.
