Nano Banana Image Examples
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The post showcases impressive examples of images generated by Nano Banana, a Google Gemini Image Generation model, sparking discussion on its capabilities, limitations, and potential applications.
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There used to be a job people would do, where they'd go around in the morning and wake people up so they could get to work on time. They were called a "knocker-up". When the alarm clock was invented, these people lose their jobs to other knockers-up with alarm clocks, they lost their jobs to alarm clocks.
You can paint your own walls or fix your own plumbing, but people pay others instead. You can cook your food, but you order take-out. It's not hard to sew your own clothes, but...
So no, I don't think it's as simple as that. A lot of people will not want the mental burden of learning a new tool and will have no problem paying someone else to do it. The main thing is that the price structure will change. You won't be able to charge $1,000 for a project that takes you a couple of days. Instead, you will need to charge $20 for stuff you can crank out in 20 minutes with gen AI.
That said, I'm pretty sure the market for professional photographers shrank after the digital camera revolution.
Since the page doesn't mention it, this is the Google Gemini Image Generation model: https://ai.google.dev/gemini-api/docs/image-generation
Good collection of examples. Really weird to choose an inappropriate for work one as the second example.
The second example under "Case 1: Illustration to Figure" is a panty shot.
I have no idea how people think they can interact with an art related product with this kind of puritanical sensibility.
Came within striking distance of OpenAI gpt-image-1 at only one point less.
Is this model open? Open weights at least? Can you use it commercially?
The best multimodal models that you can run locally right now are probably Qwen-Edit 20b, and Kontext.Dev.
https://qwenlm.github.io/blog/qwen-image-edit
https://bfl.ai/blog/flux-1-kontext-dev
[1] https://deepmind.google/models/gemma
[2] https://huggingface.co/google/gemma-7b [2]
I recently finished putting together an Editing Comparison Showdown counterpart where the focus is still adherence but testing the ability to make localized edits of existing images using pure text prompts. It's currently comparing 6 multimodal models including Nano-Banana, Kontext Max, Qwen 20b, etc.
https://genai-showdown.specr.net/image-editing
Gemini Flash 2.5 leads with a score of 7 out of 12, but Kontext comes in at 5 out of 12 which is especially surprising considering you can run the Dev model of it locally.
I agree with your assessment - even though it does tend to make changes at a global level you can least attempt to minimize its alterations through careful prompting.
Don't know if it's the same for others, but my issue with Nano Banana has been the opposite. Ask it to make x significant change, and it spits out what I would've sworn is the same image. Sometimes randomly and inexplicably it spits our the expected result.
Anyone else experiencing this or have solutions for avoiding this?
Most models (gpt-image-1, Kontext, etc) typically fail by doing the wrong thing.
From my testing this seems to be a Nano-Banana issue. I've found you can occasionally work around it by adding far more explicit directives to the prompt but there's no guarantee.
https://seed.bytedance.com/en/seedream4_0
By the way, some of the results look a little weird to me, like the one for the 'Long Neck' prompt. The giraffe of Seedream just lowered its head but its neck didn't shorten as expected. I'd like to learn about the evaluation process, especially whether it is automatic or manual.
To answer your question, all of the evaluations are performed manually. On the trickier results I'll occasionally conscript some friends to get a group evaluation.
The bottom section of the site has an FAQ that gives more detail, I'll include it here:
It's hard to define a discrete rubric for grading at an inherently qualitative level. To keep things simple, this test is purely PASS/FAIL - unsuccessful means that the model NEVER managed to generate an image adhering to the prompt.
In many cases, we often attempt a generous interpretation of the prompt - if it gets close enough, we might consider it a pass.
To paraphrase former Supreme Court Justice Potter Stewart, "I may not be able to define a passing image, but I know it when I see it."
Don’t know what’s the reason: my bad prompting or these models being tuned to work with photos/illustrations only
We're reliant on training data too.
You don't have to pay to look at food in a grocery, or a recipe in a library you go home and cook. You do pay for ingredients you use up. Unless the book you looked at, or YouTube video you aped, trained you to garden.
I’ve no idea how to even check. According to various tests I believe I have aphantasia. But mostly I’ve got not even a slightest idea on how not having it is supposed to work. I guess this is one of those mysteries when a missing sense cannot be described in any manner.
Without aphantasia, it should be easy to "see" where the dots are since your mind has placed them on the apple somewhere already. Maybe they're in a line, or arranged in a triangle, across the middle or at the top.
How do people with aphantasia answer the question?
I hadn't really placed those three dots in a specific place on the apple. But when you ask where they are, I'll decide to put them in a line on the apple. If you ask what color they are, I'll have to decide.
That is, they have to ascribe a placement rather than describe one in the image their mind conjured up.
Edit: This iDevice really wants to capitalise Apple.
Then maybe, at least in my case, it is my inability to focus my imagination when my senses are already being bombarded with external stimuli. But I cannot speak for anyone else.
To answer the question I imagine an apple with three dots in a triangle, closely together. There is no color because there is no real image, it’s just an idea. As other have said if prompted the idea gets more detailed.
That said, when I tried to learn building mind palaces it has worked. I can “walk through” places I know just fine, even recall visual details like holes in a letterbox. But again, there is no image.
In my conscious experience I pretty much imagine {apple, dot, dot, dot}. I don't "see" blue, the dots are tagged with dot.color == blue.
When you ask about the arrangement of the dots, I'll THEN think about it, and then says "arranged in a triangle." But that's because you've probed with your question. Before you probed, there's no concept in my mind of any geometric arrangement.
If I hadn't been prompted to think / naturally thought about the color of the apple, and you asked me "what color is the apple." Only then would I say "green" or "red."
If you asked me to describe my office (for example) my brain can't really imagine it "holistically." I can think of the desk and then enumerate it's properties: white legs, wooden top, rug on ground. But, essentially, I'm running a geometric iterator over the scene, starting from some anchor object, jumping to nearby objects, and then enumerating their properties.
I have glimpses of what it's like to "see" in my minds eye. At night, in bed, just before sleep, if I concentrate really hard, I can sometimes see fleeting images. I liken it to looking at one of those eye puzzles where you have to relax your eyes to "see it." I almost have to focus on "seeing" without looking into the blackness of my closed eyes.
Like they'll start at an arm and move along filling the rest of the body correctly the first time. No sketching, no finding the lines, just a human printer.
No one really sees 3d pictures in their head in HD
It's equally astonishing to me that others are different.
Do you see these pictures the same as if you were watching an HD TV?
I'm going to guess no. You don't see literally high def pictures in your head.
There are people who actually "see" a full-ass movie in their head when they read.
These are also the people who get REALLY angry when some live-action casting choice isn't exactly like in the book. I just go "meh", because I kinda remember the main character had red hair and a scar and that's it. :D
It not just images either, it's short videos.
What's interesting though is that the "video" can be missing details that I will "hallucinate" back in that will be incorrect. So I cannot always fully trust these. Like cutting the apple in half lead to a ~1/8th slice missing from one of the halves. It's weird.
You may notice when doing the apple test, once you try and define a texture, your brain adding things you think should be there.
Scared the crap out of me a few years ago when I realized I had it. Came to grips with it now.
It's interesting that if non-aphantasia people are so common, I wonder why so few paintings have scenery based solely on imagination. I even remember asking a person who paints (not in the context of this condition) how hard it was for him to paint something not directly before his eyes, but from imagination, and why he didn't do it more often. I recall that he definitely did this (painting from imagination) rarely or not at all, and the question really puzzled him
I have had some people claim to me that they can literally see what they are imagining as if it is in front of them for prolonged periods of time, in a similar way to how it would show up via AR goggles.
I guess this is a spectrum and it's tough to dealineate the abilities. But I just looked it up and what I am describing is hyperphantasia.
I have aphantasia and my dog isn't anywhere. It's just a dog, you didn't ask me to visualize anything else.
When you ask about details, like color, tail length, eyes then I have to make them up on the spot. I can do that very quickly but I don't "see" the good boy.
There are now dozens of copyright safe image and video models: Adobe, MoonValley, etc.
We technically never need human works again. We can generate everything synthetically (unreal engine, cameras on a turn table, etc.)
The physics of optics is just incredibly easy to evolve.
Not sure about that. Humans are doing almost all the work now still.
Nano banana saves literally millions of manual human pixel pushing hours.
It's easy to hate on LLMs and AI hype, but image models are changing the world and impacting every visual industry.
At the low, low cost of burning incredible amounts of energy!
This is also he same logic as “lost sale” software piracy calculations. 90% of those claimed hours would not have been spent if the tool did not exist. Most of the generated images are idle throwaways that no human would bother with creating.
Your arguments sound to me as if you're saying the cotton gin is a bad idea because 90% of that cotton wouldn't have been picked.
Lay people have been starved from being able to visually articulate themselves. We're entering into a world where everyone will have spatial articulation. That's a good thing.
Don't be the latin clergy arguing the populace shouldn't be able to read or have books.
I don't think you're being fair here at all. The technology has demonstrable positive use cases.
Conscious intelligence has not.
As another argument, we've had mathematical descriptions of optics, drawing algorithms, fixed function pipeline, ray tracing, and so much more rich math for drawing and animating.
Smart, thinking machines? We haven't the faintest idea.
Progress on Generative Images >> LLMs
Three times, something like intelligence has evolved - in mammals, octopuses, and corvids. Completely different neural architectures in those unrelated speces.
[1] https://homepage.uni-tuebingen.de/andreas.nieder/Nieder%20(2...
Even with what we've got, it took us hundreds of thousands of years to invent indoor plumbing.
Vision, I still submit, is much simpler than "intelligence". It's evolved independently almost a hundred times.
It's also hypothesized that it takes as few as a hundred thousand years to evolve advanced eye optics:
https://royalsocietypublishing.org/doi/10.1098/rspb.1994.004...
Even plants can sense the visual and physical world. Three dimensional spatial relationships and paths and rays through them are not hard.
AI is like Batman, useless without his money and utility belt. Your own abilities are more like Superman, part of who you are and always with you, ready for use.
You are now marvelling at someone taking the collective output of humans around the world, then training a model on it with massive, massive compute… and then having a single human compete with that model.
Without the human output on the Internet, none of this would be possible. ImageNet was positively small compared to this.
But yeah, what you call “imagination” is basically perturbations and exploration across a model that you have in your head, which imposes constraints (eg gravity etc) that you learned. Obviously we can remix things now that they’re on the Internet.
Having said that, after all that compute, the models had trouble rendering clocks that show an arbitrary time, or a glass of wine filled to the brim.
I know you're probably talking about analog clocks, but people when dreaming have trouble representing stable digits on clocks. It's one of the methods to tell if you are dreaming.
Arguably, if creating an art style is simply a matter of novel mechanics and uniqueness, LLMs could already do that simply by adding artists to the prompts ("X" in the style of "A" and "B") and plenty of people did (and do) argue that this is no different than what human artists do (I would disagree.) I personally want to argue that intentionally matters more than raw technique, but Hacker News would require a strict proof for the definition of intentionality that they would argue humans don't possess, but somehow LLMs do, and that of course I can't provide.
I guess I have no argument besides "it means more to me that a person does it than a machine." It matters to me that a human artist cares. A machine doesn't care. And yes, in a strictly materialist sense we are nothing but black boxes of neurons receiving stimuli and there is no fundamental difference between a green field and a cold steel rail, it's all just math and meat, but I still don't care if a machine makes X in the style of (Jack Kirby AND Frank Miller.)
I'd disagree. Art styles are a category of many similar works in relation to others or a way of bringing about similar works. They usually build off of or are influenced by prior work and previous methods, even in cases where there is a effort to avoid or subvert them. Even with novel techniques or new mediums. "Great Artists Steal" and all that.
Some people become known for certain mediums or the inclusion of specific elements, but few of them were the first or only artists to use them. "Art in the style of X" just comes down to familiarity/marketing. Art develops the way food does with fads, standards, cycles, and with technology and circumstance enabling new things. I think evolution is a pretty good analogy although it's driven by a certain amount of creativity, personal preference, and intent in addition to randomness and natural selection.
Computers could output random noise and in the process eventually end up creating an art style, but it'd take a human to recognize anything valuable and artists to incorporate it into other works. Right now what passes for AI is just remixing existing art created by humans which makes it more likely to blindly stumble into creating some output we like, but inspiration can come from anywhere. I wouldn't be surprised if the "AI Slop" art style wasn't already inspiring human artists. Maybe there are already painters out there doing portraits of people with the wrong number of fingers. As AI is increasingly consuming it's own slop things could get weird enough to inspire new styles, or alternately homogenized into nothing but blandness.
People adopt art styles because they like something about them, the aesthetic or what they represent. I don't think there are enough human artists who like AI slop (they tend to despise it categorically) enough to want to imitate it, unless it's as some form of satire. They aren't going to do so simply because it exists.
"To see a world in a grain of sand And a heaven in a wild flower..."
We - humans - have reasons to be. We get to look at a sunset and think about the scattering of light and different frequencies and how it causes the different colors. But we can also just enjoy the beauty of it.
For me, every moment is magical when I take the time to let it be so. Heck, for there to even be a me responding to a you and all of the things that had to happen for Hacker News to be here. It's pretty incredible. To me anyway.
Almost all "human" interaction online will be subject to doubt soon enough.
Hard to be cheerful when technology will be a net negative overall even if it benefits some.
Hopefully you understand the sentiment of my original message, without getting into the semantics. AI advancement, like email when it arrived, are gonna turbocharge the negatives. Difference is in the magnitude of the problem. We're dealing with whole different scale we have never seen before.
Re: Most of my emails at this point are spams. - 99% of my emails are not spam. Yet AI spam is everywhere else I look online.
..guess that's solved now.. overnight. Mindblowing
...the technical graphics (especially text) is generally wrong. Case 16 is an annotated heart and the anatomy is nonsensical. Case 28 with the tallest buildings has the decent images, but has the wrong names, locations, and years.
Case 8 Substitute for ControlNet
The two characters in the final image are VERY obviously not in the instructed set of poses.
so many little details off when the instructions are clear and/or the details are there. Brad Pitt jeans? The result are not the same style and missing clear details which should be expected to just translate over.
Another one where the prompt ended with output in a 16:9 ratio. The image isn't in that ratio.
The results are visually something but then still need so much review. Can't trust the model. Can't trust people lazily using it. Someone mentioned something about 'net negative'.
The way current AI is set up, you can't even reliably adjust the position of the sun.
Also, of course: https://en.wikipedia.org/wiki/All_models_are_wrong
- Given a face shot in direct sunlight with severe shadows, it would not remove the shadows
- Given an old black and white photo, it would not render the image in vibrant color as if taken with a modern DSLR camera. It will colorize the photo, but only with washed out, tinted colors
- When trying to reproduce the 3 x 3 grid of hair styles, it repeatedly created a 2x3 grid. Finally, it made a 3x3 grid, but one of the nine models was black instead of caucasian.
- It is unable to integrate real images into fabricated imagery. For example, when given an image of a tutu and asked to create an image of a dolphin flying over clouds wearing the tutu, the result looks like a crude photoshop snip and copy/paste job.
The "Photos of Yourself in Different Eras" one said "Don't change the character's face" but the face was totally changed. "Case 21: OOTD Outfit" used the wrong camera. "Virtual Makeup Try-On" messed up the make up. "Lighting Control" messed up the lighting, the joker minifig is literally just SH0133 (https://www.bricklink.com/catalogItemInv.asp?M=sh0133), "Design a Chess Set" says you don't need an input image, but the prompt said to base it off of a picture that wasn't included and the output is pretty questionable (WTF is with those pawns!), etc.
I mean, it's still pretty neat, and could be useful for people without access to photoshop or to get someone started on a project to finish up by hand.
I don't know of a demo, image, film, project or whatever where the showoff pieces are not cherry picked.
I uploaded an image I found of Midtown Manhattan and tried various times to get it to highlight the Chrysler Building, it claimed it wasn't in the image (it was). I asked it to do 432 Park Ave, and it literally inserted a random building in the middle of the image that was not 432 Park, and gave me some garbled text for the description. I then tried Chicago as pictured from museum campus and asked it to highlight 2 Prudential, and it inserted the Hancock Center, which was not visible in the image I uploaded, and while the text was not garbled, was incorrect.
I get far better results using ChatGPT for example. Of course, the character seldom looks anything like the reference, but it looks better than what I could do in paint in two minutes.
Am I using the wrong model, somehow??
I understand the results are non deterministic but I get absolute garbage too.
Uploaded pics of my (32 years old) wife and we wanted to ask it to give her a fringe/bangs to see how would she look like it either refused "because of safety" and when it complied results were horrible, it was a different person.
After many days and tries we got it to make one but there was no way to tweak the fringe, the model kept returning the same pic every time (with plenty of "content blocked" in between).
It's also cheaper than Gemini, and has way fewer spurious content warnings, so overall I'm done with Gemini
When Nano Banana works well, it really works -- but 90% of the time the results will be weird or of poor quality, with what looks like cut-and-paste or paint-over, and it also refuses a lot of reasonable requests on "safety" grounds. (In my experience, almost anything with real people.)
I'm mostly annoyed, rather than impressed, with it.
I was a bit surprised to see quality. Last time I played around with image generation is a few months back and I’m more in the frustration camp. Not to say that I believe some people with more time and dedication at their hand can tickle better results.
[0] https://imgur.com/a/aSbOVz5
which goes to show that some of these amazing results might need 18 attempts and such.
I then tried to generate some multi-angle product shots from a single photo of an object, and it just refused to do the whole left, right, front, back thing, and kept doing things like a left, a front, another left, and weird half back/half side view combination.
Very frustrating.
I had them before when I was trying this and yes, I had them turned off.
I use the API directly but unless I'm having a "Berenstein Bears moment" I could have sworn those safety settings existed under the Advanced Options in AI Studio a few weeks ago.
[1] https://github.com/JimmyLv/awesome-nano-banana
Huge thanks to the author (and the many contributors) as well for gathering so many examples; it’s incredibly useful to see them to better understand the possibilities of the tool.
Through that testing, there is one prompt engineering trend that was consistent but controversial: both a) LLM-style prompt engineering with with Markdown-formated lists and b) old-school AI image style quality syntatic sugar such as award-winning and DSLR camera are both extremely effective with Gemini 2.5 Flash Image, due to its text encoder and larger training dataset which can now more accurately discriminate which specific image traits are present in an award-winning image and what traits aren't. I've tried generations both with and without those tricks and the tricks definitely have an impact. Google's developer documentation encourages the latter.
However, taking advantage of the 32k context window (compared to 512 for most other models) can make things interesting. It’s possible to render HTML as an image (https://github.com/minimaxir/gemimg/blob/main/docs/notebooks...) and providing highly nuanced JSON can allow for consistent generations. (https://github.com/minimaxir/gemimg/blob/main/docs/notebooks...)
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