I've been playing with it for the last hour or so. I immediately updated my platform to use both since 2.5 is my primary model (i run a prompt->design site).
After playing with it for an hour, I'm rolling back 2.6-flare to 2.5-flare. I've found 2.6-flare specifically to be too simplistic in its approaches.
What's the state of the art for image gen? I'm far removed and pretty happy with the free Gemini tier but its notable to me that I rarely see boasting about being "the best image generator yet" and I wonder if that's because they're all just good enough?
It has to be Google, since OpenAI always compares to other companies when it feels its winning at some benchmarks. They didn't even compare on cost, speed, or any benchmarks at all.
I would caution against using the webapp to test the quality of this new model because OpenAI does not indicate which image model and effort is being used to generate the image, to my continued frustration.
Using the grid trick for animations to generate the individual frames has been around for awhile and not specific to ChatGPT: I'm surprised it took this long for one of the official providers to make it explicit. However, adherence between frames is generally tricky, and even in the examples there is a lot of noise between frames although that could be due to compression.
SVG support is interesting but I suspect shenanigans are afoot since that's not how image generation models work, and it would likely strip any SynthID watermarks. It's possible OpenAI has pipelined a raster-to-vector tool that does the conversion automatically. (I developed a similar pipeline and it works extremely well; I'll open source it at some point)
OpenAI claims 50% faster generation with Images 2.6 but curiously price is the same as Image 2.5.
I wonder if there's some weird accounting going on, or if this is real. It would mean around 24M images an hour and that's in the area of "more images than anyone will see". It means there's lots of wasted automated effort in this process somewhere, even if we assume some creators will request 8x variants of their ideas.
That depends on your definition of pixel art. (people are exceptionally pedantic about this)
With the right prompt engineering, it (and similar quality models) can indeed do relevant pixel art, but due to the existence of blotchiness/SynthID watermarks you'll need to do some sort of postprocessing regardless.
After playing with it for an hour, I'm rolling back 2.6-flare to 2.5-flare. I've found 2.6-flare specifically to be too simplistic in its approaches.
Examples: https://diffui.ai/app/canvas/178db5d5-ff87-4b43-aa98-2897773...
2.6-sunburst seems solid however.
I guess I should have been careful about “half”?
These models have a hard time imagining men with naturally blond hair getting grey.
That said, I gave it one of my more difficult image prompts and the model now surprisingly passes (without plagiaristic cheating): https://chatgpt.com/share/6aebcbcd-6b8c-83e8-b52b-dbedf20ce4...
SVG support is interesting but I suspect shenanigans are afoot since that's not how image generation models work, and it would likely strip any SynthID watermarks. It's possible OpenAI has pipelined a raster-to-vector tool that does the conversion automatically. (I developed a similar pipeline and it works extremely well; I'll open source it at some point)
OpenAI claims 50% faster generation with Images 2.6 but curiously price is the same as Image 2.5.
With the right prompt engineering, it (and similar quality models) can indeed do relevant pixel art, but due to the existence of blotchiness/SynthID watermarks you'll need to do some sort of postprocessing regardless.
SVG is cool though, I wonder how good it is.