A real photo of an iPhone SE on the left and a ChatGPT 4o-generated one on the right. Thanks, Sam Altman, for giving us access to ChatGPT ‘s new integrated image-generation skills. They’re, as Steve Jobs might’ve described them, insanely good.
So good, in fact, that I’m worried now about my little corner of the universe where we try to discern the accuracy of renders, models, and pre-production leaks that might tell us the tale of future Apple products, like the rumored iPhone 17 Air .
For those who don’t know, the iPhone 17 Air (or Slim) is the oft-talked-about but never-confirmed ultra-slim iPhone 16 Plus/iPhone 16e/SE hybrid that could be the most exciting iPhone update when Apple likely unveils a whole new iPhone 17 line in September.
While it may not be the most powerful iPhone, it should be the biggest and thinnest of the bunch. Even the single rear camera might not be enough to keep potential buyers away.
Imagining what it could look like, well, that’s my job. Or it was until I started working with ChatGPT running the recently updated 4o model , which is capable of generating images out of thin air or based on photos and images you upload into it.
It’s a slightly methodical model, taking up to 45 seconds to generate an image that flows in slowly, almost one microscopic, horizontal line of pixels at a time. The results are something to behold.
It’s not just the quality but how ChatGPT can maintain the thread and cohesion of images from prompt to prompt. Usually, if you start with image generation in something like OpenAI ‘s Dall-E or, say, X’s Grok , it’ll do a good job with the first image.
However, when you request changes, elements of the original disappear or end up altered. It’s even harder to create a series of images that appear to be part of the same story or theme. There are usually too many differences.
ChatGPT 4o image generation appears different and, possibly, more capable. ChatGPT 4o did a nice anime glow up with my picture of a hawk (left). Having already experimented a […]

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