Why GPT Image 2 Finally Fixes the One Thing That Made AI Images Unusable for UI Work

I Built a Playground for GPT Image 2 Because I Was Tired of Fixing AI-Generated Typography
For the past two years, my design workflow had a weird bottleneck: every time I used AI to generate a marketing image, I would immediately open Figma and drop a rectangle over the text area.
Not because I wanted to. Because I had to.
You know the problem. Ask Midjourney or Stable Diffusion for a poster with a headline, and you get something that looks gorgeous from ten feet away. Zoom in, and the text is either complete gibberish or a haunting approximation of a word—like "Wclcvm" instead of "Welcome." Six letters, four correct. Almost respectful.
Chinese text was even worse. I once needed a cover image for a WeChat article. The prompt asked for a restaurant banner with "老北京炸酱面" in traditional signage style. The model produced something that looked like four different ancient scripts having an argument. Beautiful composition, utterly unreadable text. So I generated the background, exported to Photoshop, and typed the characters myself. The AI "saved" me negative five minutes.
This is why I was skeptical when people started talking about GPT Image 2 and its supposed text-rendering improvements. I had been burned too many times.
But the early tests were genuinely surprising.
The first thing I noticed was that text no longer looked like a sticker slapped on top of a painting. I ran a prompt for a bilingual restaurant menu—Chinese and English, prices, item descriptions. Previously, this was a guaranteed disaster zone. GPT Image 2 kept the layout coherent. The Chinese characters weren't scrambled. The English wasn't missing letters. The font sizes matched the visual hierarchy of the page. It wasn't perfect, but it was usable.
Then I tried UI mockups. I asked for a Slack-style chat interface: channel list on the left, message bubbles, timestamps, and an input field. Before, the text areas in these generations were always smudged color blocks. This time, the channel names had actual letters. The timestamps read like timestamps. The placeholder text in the input bar was readable. If you squinted, you could almost believe it was a real screenshot.
Chinese rendering was the real shocker, though. I generated a Beijing hutong night scene with a neon sign that needed to say "老北京炸酱面." Not only were the characters intact, but the font style matched the gritty, hand-painted aesthetic of the alley. It wasn't a sterile system font dropped onto a photo. It felt like it belonged there.
Is it flawless? No. Long paragraphs still get weird. Overly decorative typefaces can break down. But we've moved from "completely unusable" to "use it as-is for most pitches and presentations." That's a massive jump.
Because I was running so many of these tests across different scenarios—posters, app screenshots, bilingual layouts, product photography with labels—I ended up building a dedicated playground to keep everything organized. If you want to test GPT Image 2's text handling without setting up local pipelines or burning through API credits guessing at prompts, you can just head to jptimagine2.com. It has free daily credits, supports image-to-image if you want to iterate on a base concept, and the whole thing is set up specifically for text-heavy generation workflows. No sign-up required to start poking around.
Here's the thing about AI image generation: "looks stunning" and "actually works" are two different bars. A hyper-realistic portrait is impressive, but if you're building real products, you probably need images with readable labels, coherent UI text, or multilingual signage. GPT Image 2 isn't just better at art. It's better at the boring, practical stuff that makes an image usable in a real workflow.
That might not be as flashy as 8K fantasy landscapes. But for anyone shipping actual designs, it's the difference between "nice demo" and "ship it."