If Nano-Banana-pro with Veo 3.1 existed during my PhD, I would’ve finished a 6-year dissertation in a single year — it’s generating ideas today that used to take me 18 months just to convince people were possible.
Nano Banana Pro
311–320 of 718 posts
Re: Nano Banana Pro
#312Something I find weird about AI image generation models is that even though they no longer produce weird "artifacts" that give away that the fact that it was AI generated, you can still recognize that it's AI due to stylistic choices. Not all examples they gave were like this. The example they gave of the word "Typography" would have fooled me as human-made. The infographics stood out though. I would have immediately…
It's a bit odd to say, but another big clue identifying something as AI-generated is that it simply looks "too good" for what it is being used for. If I see a little info graphic demonstrating something relatively mundane, and it has nice 3D rendered characters or graphical elements, at this point it's basically guaranteed to be AI, because you just sort of intuitively know when something would've justified the human labor necessary to produce that.
Re: Nano Banana Pro
#313Google has been stomping around like Godzilla this week, and this is the first time I decided to link my card to their AI studio. I had seen people saying that they gave up and went to another platform because it was "impossible to pay". I thought this was strange, but after trying to get a working API key for the past half hour, I see what they mean. Everything is set up, I see a message that says "You're using Paid…
100% this. I am using the pro/max plans on both claude and openai. Would love to experiment with gemini but paying is next to impossible. Why do i need the risk of a full blown gcp project just to test gemini. No thx.
Re: Nano Banana Pro
#314Earlier quoted context omitted.
It’s subtly incorrect. R/w permissions for example are described incorrectly on some nodes.
Then the question becomes, can it incorporate targeted feedback, or is it a oneshot-or-bust affair? My experience is that ChatGPT is very good at iterating on text (prose, code) but fairly bad at iterating on images. It struggles to integrate small changes, choosing instead to start over from scratch, with wildly different results. Thinking especially here of architectural stuff, where it does a great job laying out…
You can use targeted feedback - but it's on the user to verify whether the edits were completely localized. In my experience NB mostly tends to make relatively surgical edits but if you're not careful it'll introduce other minute changes.
And that point you can either start over or just feather/mask with the original in any Photoshop type application.
Re: Nano Banana Pro
#315Re: Nano Banana Pro
#316Everyone who worked on this is a traitor to the human race. Why do we need to make it impossible to make a living as an artist? Who thinks an endless tsunami of garbage “content” churned out by machines dropping the bottom out of all artistic disciplines is a good idea?
Capitalism, at work. Wherever there is a cost, there will be attempts made at cost efficiency. Google understands that hiring designers or artists is expensive, and they want to offer a cheaper, more effective alternative so that they can capture the market.
In a coffee shop this morning I saw a lady drawing tulips with a paper and pencil. It was beautiful, and I let her know... But as I walked away I felt sad that I don't feel that when browsing online anymore- because I remember how impressive it used to feel to see an epic render, or an oil painting, etc... I've been turned cynical.
Re: Nano Banana Pro
#317Something I find weird about AI image generation models is that even though they no longer produce weird "artifacts" that give away that the fact that it was AI generated, you can still recognize that it's AI due to stylistic choices. Not all examples they gave were like this. The example they gave of the word "Typography" would have fooled me as human-made. The infographics stood out though. I would have immediately…
We are just very sharp when it comes to seeing small differences in images.
I'm reminded of when the air force decided to create a pilot seat that worked for everyone. They took the average body dimensions of all their recruits and designed a seat to fit the average. It turned out, the seat fit none of their recruits. [1]
I think AI image generation is a lot like this. When you train on all images, you get to this weird sort of average space. AI images look like that, and we recognize it immediately. You can prompt or fine tune image models to get away from this, though -- the features are there it's a matter of getting them out. Lots of people trying stuff like this: https://www.reddit.com/r/StableDiffusion/comments/1euqwhr/re..., the results are nearly impossible to distinguish from real images.
[1] https://www.thestar.com/news/insight/when-u-s-air-force-disc...
Re: Nano Banana Pro
#318Earlier quoted context omitted.
"Plug into right power socket" Same language, opposite meaning because of a particular noun + context. I think the only thing obvious here is that there is no obvious solution other than adding lots of clarification to your prompt.
I think you missed the entire point?
No, they just disagree with you.
Re: Nano Banana Pro
#319Earlier quoted context omitted.
>This is a GREAT example of the (not so) subtle mistakes AI will make in image generation, or code creation, or your future knee surgery. The mistake is in the prompting (not enough information). The AI did the best it could "What's the biggest known planet" "Jupiter" "NO I MEANT IN THE UNIVERSE!"
No, this is squarely on the AI. A human would know what you mean without specific instructions.
If you asked me right now what the biggest known planet was, I'd think Jupiter. I'd assume you were talking about our solar system ("known" here implying there might be more planets out in the distant reaches).