Gemini 2.5 Flash Image
developers.googleblog.com
developers.googleblog.com
Just search nano banana on Twitter to see the crazy results. An example. https://x.com/D_studioproject/status/1958019251178267111
There is a whole spectrum of potential sketchiness to explore with these, since I see a few "sign in with Google" buttons that remind me of phishing landing pages.
No it's not.
We've had rich editing capabilities since gpt-image-1, this is just faster and looks better than the (endearingly? called) "piss filter".
Flux Kontext, SeedEdit, and Qwen Edit are all also image editing models that are robustly capable. Qwen Edit especially.
Flux Kontext and Qwen are also possible to fine tune and run locally.
Qwen (and its video gen sister Wan) are also Apache licensed. It's hard not to cheer Alibaba on given how open they are compared to their competitors.
We've left the days of Dall-E, Stable Diffusion, and Midjourney of "prompt-only" text to image generation.
It's also looking like tools like ComfyUI are less and less necessary as those capabilities are moving into the model layer itself.
Gpt4 isn't "fundamentally different" from gpt3.5. It's just better. That's the exact point the parent commenter was trying to make.
My test is going to https://unsplash.com/s/photos/random and pick two random images, send them both and "integrate the subject from the second image into the first image" as the prompt. I think Gemini 2.5 is doing far better than ChatGPT (admittedly ChatGPT was the trailblazer on this path). FluxKontext seems unable to do that at all. Not sure if I were using it wrong, but it always only considers one image at a time for me.
Edit: Honestly it might not be the 'gpt4 moment." It's better at combining multiple images, but now I don't think it's better at understanding elaborated text prompt than ChatGPT.
Flux Kontext is an editing model, but the set of things it can do is incredibly limited. The style of prompting is very bare bones. Qwen (Alibaba) and SeedEdit (ByteDance) are a little better, but they themselves are nowhere near as smart as Gemini 2.5 Flash or gpt-image-1.
Gemini 2.5 Flash and gpt-image-1 are in a class of their own. Very powerful instructive image editing with the ability to understand multiple reference images.
> Edit: Honestly it might not be the 'gpt4 moment." It's better at combining multiple images, but now I don't think it's better at understanding elaborated text prompt than ChatGPT.
Both gpt-image-1 and Gemini 2.5 Flash feel like "Comfy UI in a prompt", but they're still nascent capabilities that get a lot wrong.
When we get a gpt-image-1 with Midjourney aesthetics, better adherence and latency, then we'll have our "GPT 4" moment. It's coming, but we're not there yet.
They need to learn more image editing tricks.
I feel like most of the people on HN are paying attention to LLMs and missing out on all the crazy stuff happening with images and videos.
LLMs might be a bubble, but images and video are not. We're going to have entire world simulation in a few years.
It's not even close. https://twitter.com/fareszr/status/1960436757822103721
It took me a LOT of time to get things right, but if I was to get an actual studio to make those images, it would have cost me a thousands of dollars
But flash 2.5? Worked! It did it, crazy stuff
https://imgur.com/a/internet-DWzJ26B
Anyone can make images and video now.
- Midjourney (background)
- Qwen Image (restyle PG)
- Gemini 2.5 Flash (editing in PG)
- Gemini 2.5 Flash (adding YC logo)
- Kling Pro (animation)
I didn't spend too much time correcting mistakes.
I used a desktop model aggregation and canvas tool that I wrote [1] to iterate and structure the work. I'll be open sourcing it soon.
I couldn't get the 3d thing to do much. I had assets in the scene but I couldn't for the life of me figure out how to use the move, rotate or scale tools. And the people just had their arms pointing outward. Are you supposed to pose them somehow? Maybe I'm supposed to ask the AI to pose them?
Inpainting I couldn't figure out either... It's for drawing things into an existing image (I think?) but it doesn't seem to do anything other than show a spinny thing for awhile...
I didn't test the video tool because I don't have a midjourney account.
(But yeah, some got a generator attached...)
The old top of the game is available to more people (though mid level people trying to level up now face a headwind in a further decoupling of easily read signals and true taste, making the old way of developing good taste harder).
This stuff makes people who were already "master rate" who are also nontrivially sophisticated machine learning hobbyists minimum and drives their peak and frontier out, drives break even collaboration overhead down.
It's always been possible to DIY code or graphic design, it's always been possible to tell the efforts of dabblers and pros apart, and unlike many commodities? There is rarely a "good enough". In software this is because compute is finite and getting more out of it pays huge, uneven returns, in graphic design its because extreme quality work is both aesthetically pleasing as well as a mark of quality (imperfect but a statement someone will commit resources).
And it's just hard to see it being different in any field. Lawyers? Opposing counsel has the best AI, your lawyer better have it too. Doctors? No amount of health is "enough" (in general).
I really think HN in particular but to some extent all CNBC-adjacent news (CEO OnlyFans stuff of all categories) completely misses the forest (the gap between intermediate and advanced just skyrocketed) for the trees (space-filling commodity knowledge work just plummeted in price).
But "commodity knowledge work" was always kind of an oxymoron, David Graeber called such work "bullshit jobs". You kinda need it to run a massive deficit in an over-the-hill neoliberal society, it's part of the " shift from production to consumption" shell game. But it's a very recent, very brief thing that's already looking more than wobbly. Outside of that? Apprentices, journeymen, masters is the model that built the world.
AI enables a new even more extreme form of mastery, blurs the line between journeyman and dabbler, and makes taking on apprentices a much longer-term investment (one of many reasons the PRC seems poised to enjoy a brief hegemony before demographics do in the Middle Kingdom for good, in China, all the GPUs run Opus, none run GPT-5 or LLaMA Behemoth).
The thing I really don't get is why CEOs are so excited about this and I really begin to suspect they haven't as a group thought it through (Zuckerberg maybe has, he's offering Tulloch a billion): the kind of CEO that manages a big pile of "bullshit jobs"?
AI can do most of their job today. Claude Opus 4.1? It sounds like if a mid-range CEO was exhaustively researched and gaff immune. Ditto career machine politicians. AI non practitioner prognosticators. That crowd.
But the top graphic communications people and CUDA kernel authors? Now they have to master ComfyUI or whatever and the color theory to get anything from it that stands out.
This is not a democratizing thing. And I cannot see it accruing to the Zuckerberg side of the labor/capital divvy up without a truly durable police state. Zuck offering my old chums nation state salaries is an extreme and likely transitory thing, but we know exactly how software professional economics work when it buckets as "sorcery" and "don't bother": that's 1950 to whenever we mark the start of the nepohacker Altman Era, call it 2015. In that world good hackers can do whatever they want, whenever they want, and the money guys grit their teeth. The non-sorcery bucket has paper mache hack-magnet hackathon projects in it at a fraction of the old price. So disruption, wow.
Whether that's good or bad is a value judgement I'll save for another blog post (thank you for attending my TED Talk).
Something similar has been the case with text models. People write vague instructions and are dissatisfied when the model does not correctly guess their intentions. With image models it's even harder for model to guess it right without enough details.
Still needs more RLHF tuning I guess? As the previous version was even worse.
I didn't see it at first sight but it certainly is not the same jacket. If you use that as an advertisement, people can sue you for lying about the product.
But look at that example. With this new frontier of AI, that world class engineering talent can finally be put to use…for product placement. We’ve come so far.
Did you think that Google would just casually allow their business to be disrupted without using the technology to improve the business and also protecting their revenue?
Both Meta and Google have indicated that they see Generative AI as a way to vertically integrate within the ad space, disrupting marketing teams, copyrighters, and other jobs who monitor or improve ad performance.
Also FWIW, I would suspect that the majority of Google engineers don't work on an ad system, and probably don't even work on a profitable product line.
“Nano banana” is probably good, given its score on the leaderboard, but the examples you show don't seem particularly impressive, it looks like what Flux Kontext or Qwen Image do well already.
https://genai-showdown.specr.net
This model gets 8 of the 12 prompts correct and easily comes within striking distance of the best-in-class models Imagen and gpt-image-1 and is a significant upgrade over the old Gemini Flash 2.0 model. The reigning champ, gpt-image-1, only manages to edge out Flash 2.5 on the maze and 9-pointed star.
What's honestly most astonishing to me is how long gpt-image-1 has remained at the top of the class - closing in on half a year which is basically a lifetime in this field. Though fair warning, gpt-image-1 is borderline useless as an "editor" since it almost always changes the whole image instead of doing localized inpainting-style edits like Kontext, Qwen, or Nano-Banana.
Comparison of gpt-image-1, flash, and imagen.
https://genai-showdown.specr.net?models=OPENAI_4O%2CIMAGEN_4...
Came into this thread looking for this post. It's a great way to compare prompt adherence across models. Have you considered adding editing capabilities in a similar way given the recent trend of inpainting-style prompting?
I've done some experimentation with Qwen and Kontext and been pretty impressed, but it would be nice to see some side by sides now that we have essentially three models that are capable of highly localized in-painting without affecting the rest of the image.
> midjourney covers 9 of 8 arms with sock puppets.
Midjourney is shown as a fail so I'm not sure what your point is. And those don't even look remotely close to sock puppets, they resemble stockings at best.
Do you know of any similar sites that that compares how well the various models can adhere to a style guide? Perhaps you could add this?
I.e. pride the model with a collection of drawings in a single style, then follow prompts and generate images in the same style?
For example if you wanted to illustrate a book, and have all the illustrations look like they were from the same artists.
It's basically a necessity if you're working on something like a game or comic where you need consistency around characters, sprites, etc.
I will note that, personally, while adherence is a useful measure, it does miss some of the qualitative differences between models. For your "spheron" test for example, you note that "4o absolutely dominated this test," but the image exhibits all the hallmarks of a ChatGPT-generated image that I personally dislike (yellow, with veiny, almost impasto brush strokes). I have stopped using ChatGPT for image generation altogether because I find the style so awful. I wonder what objective measures one could track for "style"?
It reminders be a bit of ChatGPT vs Claude for software development... Regardless of how each scores on benchmarks, Claude has been a clear winner in terms of actual results.
This trend of tools that point a finger at you and set guardrails is quite frustrating. We might need a new OSS movement to regain our freedom.
So far no model is willing to clean it up :/
People sometimes do it for free ("my son died, and this is the only photo I have") or for an agreed upon tip.
Keep in mind no editing model is magic and if the pixels just aren’t there for their faces it’s essentially going to be making stuff up.
However, the results the comfyui people get are lightyears ahead of any oneshot-prompt model. Either you can find someone to do cleanup for you (should be trivial, I wouldn't pay more than $10-15) or if you have good specs for inference you could learn to do it yourself.
I just went back to the chat and asked it to remove the glasses and it worked. Asking it to remove the shirt also succeeded, although a) this is a head and shoulders photo so nothing NSFW, and b) it didn't do a great job of guessing what my shoulders look like.
In quick testing, prompt adherence does appear to be much better for massive prompt and the syntatic sugar does appear to be more effective. And there are other tricks not covered which I suspect may allow more control, but I'm still testing.
Given that generations are at the same price as its competitors, this model will shake things up.
I send him one photo a day and he's been loving it. Just a fun little thing to put a smile on his face (and mine).
It was all fun and games until the little shit crawled out of our doggy door for the first and only time when I was going to the bathroom. As I was looking for her I got a notification we were in a tornado warning.
Luckily the dog knew where she had gone and led me to her, having crawled down our (3 step) deck, across our yard, and was standing looking up at the angry clouds.
I'm scanning my parents photos at the moment.
Both Silverfast and Nikon Scan methods look great when zoomed out. I never tried Vuescan's infrared option. I just felt the positive colors it produced looks wrong/"dead".
In my eyes, one specific example they show (“Prompt: Restore photo”) deeply AI-ifies the woman’s face. Sure it’ll improve over time of course.
On free tier, I’d essentially believe that to be the default behavior. In reality they might simply use your feedback and your text prompts instead. Certainly know free Google/OpenAI LLM usage entails prompts being used for research.
Edit: decent chance it would NOT directly integrate grandma into its training, but would try hard to use an offline model for any privacy concerns
This is the first image I tried:
https://i.imgur.com/MXgthty.jpeg (before)
https://i.imgur.com/Y5lGcnx.png (after)
Sure, I could manually correct that quite easily and would do a better job, but that image is not important to us, it would just be nicer to have it than not.
I'll probably wait for the next version of this model before committing to doing it, but its exciting that we're almost there.
I've been waiting for that, too. But I'm also not interesting in feeding my entire extended family's visual history into Google for it to monetize. It's wrong for me to violate their privacy that way, and also creepy to me.
Am I correct to worry that any pictures I send into this system will be used for "training?" Is my concern overblown, or should I keep waiting for AI on local hardware to get better?
I think we will eventually have AI based tools that are just doing what a skilled human user would do in Photoshop, via tool-use. This would make sense to me. But just having AI generate a new image with imagined details just seems like waste of time.
So yeah, if I'm gonna then upscale them or "repair" them using generative AI, then it's a bit pointless to take them in the first place.
If you leave to imagination, it's likely they each imagine something different.
And further, I can imagine some person actually having such footage of themselves being threatened to be released, then using the former narrative as a cover story were it to be released. Is there anything preventing AI generated images, video, etc from being always detectible by software that can intuit if something is AI? what if random noise is added, would the "Is AI" signal persist just as much as the indication to human that the footage seems real?
If we have a trust worthy way to verify proof-of-human made content than anything missing those creds would be red flags.
https://iptc.org/news/googles-pixel-10-phone-supports-c2pa-u...
I read a scifi novel where they invented a wormhole that only light could pass through but it could be used as a camera that could go anywhere and eventually anytime and there was absolutely no way to block it. So some people adapted to this fact by not wearing clothes anymore.
Erm... What?
Although, there's also a chance that those "blackmail gangs" never materialize. After all, you could already ten years ago pay cheap labor to create reasonably good fake images using Photoshop.
It does look like I'm using the new model, though. I'm getting image editing results that are well beyond what the old stuff was capable of.
This is in stark contrast to ChatGPT, where an edit prompt typically yields both requested and unrequested changes to the image; here it seems to be neither.
In some cases you'll pass in multiple images + a prompt and get back something that's almost visually indistinguishable from just one of the images and nothing from the prompt.
Arguably they follow the prompt better than what Google is showing off, but at the same time look less impressive.
https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%...
I wonder if the bot is forced to generate something new— certainly for a prompt like that it would be acceptable to just pick the first result off a google image search and be like "there, there's your picture of a piano keyboard".
for instance:
https://aistudio.google.com/app/prompts/1gTG-D92MyzSKaKUeBu2...
So when generating a video of someone playing a keyboard the model would incorporate the idea of repeating groups of 8 tones, which is a fixed ideational aspect which might not be strongly represented in words adjacent to "piano".
It seems like models need help with knowing what should be static, or homomorphic, across or within images associated with the same word vectors and that words alone don't provide a strong enough basis [*1] for this.
*1 - it's so hard to find non-conflicting words, obviously I don't mean basis as in basis vectors, though there is some weak analogy.
Perhaps it's a second word-vector space that allows context defined associations? Maybe it just needs tighter association of piano_keyboard with 8-step_repetition??
Interestingly, it can change pages with tons of text on them without any problem, but cannot seem to do translation, if I ask it to translate a French comic page, the text ends up garbled (even though it can perfectly read and translate the text by itself).
I tried with another page, and it copypasted the same character (in different poses!) all over the panels. Absolutely uncanny!
However when I asked to remake a Western comic book in a manga style (provided a very similar manga page to the comic one), it totally failed.
Also about 50% of the time, it just tells me it'll generate the image but doesn't actually do it - not sure what's going on but a retry fixes it, but it's annoying.
It did not change the text on a hat (ended up changing 1 of 3 words).
On one occasion it regenerated the same image again, ignoring my instructions to edit.
I get the feeling that this model is optimised for images with people in it than objects or drawings etc
I see an announcement and it’s a waitlist. It says I can use it right now and I get a 404, or a waitlist, or it doesn’t work in my country. With the AI stuff more often it takes me to a place where I can do something but not what they say, and have zero information about whether I’m using the new thing or not.
Like this is flash image preview, but I have flash which is also a thing so is it the new one or not? The ui hasn’t changed but now it can do pictures so has my flash model moved from a GA model to a preview one? Probably! Or maybe it gets routed? Who knows!
It was in the endless list of new shiny 'skills' that feels good to have. Now I can use nano-banana instead. Other models will soon follow, I am sure.
Engineering probably takes a while (5 years? 10 years?) because errors multiply and technical debt stacks up.
In images, that's not so much of a big deal. You can re-roll. The context and consequences are small. In programs, bad code leads to an unmaintainable mess and you're stuck with it.
But eventually this will catch up with us too.
If you think that these tools don't automate most existing graphics design work, you're gravely mistaken.
The question is whether this increases the amount of work to be done because more people suddenly need these skills. I'm of the opinion that this does in fact increase demand. Suddenly your mom and pop plumbing business will want Hollywood level VFX for their ads, and that's just the start.
If anything, knowing Photoshop (I use Affinity Designer/Photo these days) is actually incredibly useful to finesse the output produced by AI. No regrets.
> "had a blast"
One can have blasts in many things nowadays. Like playing Factorio, writing functional code for recreational problem solving, playing Chess, making SBC/Microprocessor projects for fun, doing Math for fun, and so on...
Photoshop just couldn’t compete with the existing blasts in my life, and I felt a little bad for not learning it. But that teeny, tiny bad feeling has been wiped away by nano-banana.
I have to say while I'm deeply impressed by these text to image models, there's a part of me that's also wary of their impact. Just look at the comments beneath the average Facebook post.
It survives a lot of transformation like compression, cropping, and resizing. It even survives over alterations like color filtering and overpainting.
Now is that so bad?
Don’t Pay This AI Family To Write You a Song - https://www.youtube.com/watch?v=u-DDHSfBBeo
For people like me that don’t know what nano-banana is.
I thought Medium was a stuck up blogging platform. Other than for paid subscriptions, why would they pay bloggers? Are they trying to become the next HuffPost or something?
"Banana" would be a nice name for their AI, and they could freely claim it's bananas.
All images created or edited with Gemini 2.5 Flash Image will include an invisible SynthID digital watermark, so they can be identified as AI-generated or edited.
Deepfakes have the potential to totally destroy the average person’s already tenuous connection with reality outside of their immediate vicinity.
Some will be fooled by an endless stream of fakes and others will never believe anything again.
Politicians will dismiss footage of them doing or saying something bad as fake. And many times, they’ll be telling the truth.
We are already living in a post-fact world to some extent, but buckle up, because it’s about to get a lot worse.
Edit: I should probably say for full transparency that I am strongly FOR watermarks for AI imagery
Imagine for example, that in the future and with improved tech, manufacturer of knifes were to embed a gps chip in all knifes sold because it might be used for dangerous things.
The worse in all of that being that the big tech does it based on their own "moral" compass and not based on a legal requirement.
Regarding the watermark, that is also applying to generated text in theory, imagine that you ask ai to refactor a job application letter or a letter to your landlord, and that Google will snitch you with watermark that you used AI for that.
I don't know if that's the argument you're trying to make, but I think it's worth considering
""" Unfortunately, I can't generate images of people. My purpose is to be helpful and harmless, and creating realistic images of humans can be misused in ways that are harmful. This is a safety policy that helps prevent the generation of deepfakes, non-consensual imagery, and other problematic content.
If you'd like to try a different image prompt, I can help you create images of a wide range of other subjects, such as animals, landscapes, objects, or abstract concepts. """
It’s possible that they relaxed the safety filtering to allow humans but forgot to update the error message.
"Unfortunately I'm not able to generate images that might cause bad PR for Alphabet(tm) or subsidiaries. Is there anything else I can generate for you?"
https://www.reddit.com/r/LocalLLaMA/comments/1mx1pkt/qwen3_m...
...
Service Cost per Image Cost per 1,000 Images
Flux Schnell $0.003 $3.00
Gemini 1.5 Flash $0.039 $39.00Without going into detail, basically the task boils down to, "generate exactly image 1, but replace object A with the object depicted in image 2."
Where image 2 is some front-facing generic version, ideally I want the model to place this object perfectly in the scene, replacing the existing object, that I have identified ideally exactly by being able to specify its position, but otherwise by just being able to describe very well what to do.
For models that can't accept multiple images, I've tried a variation where I put a blue box around the object that I want to replace, and paste the object that I want it to put there at the bottom of the image on its own.
I've tried some older models, and ChatGPT, also qwen-image last week, and just now, this one. They all fail at it. To be fair, this model got pretty damn close, it replaced the wrong object in the scene, but it was close to the right position, and the object was perfectly oriented and lit. But it was wrong. (Using the bounding box method.. it should have been able to identify exactly what I wanted to do. Instead it removed the bounding box and replaced a different object in a different but close-by position.)
Are there any models that have been specifically trained to be able to infill or replace specific locations in an image with reference to an example image? Or is this just like a really esoteric task?
So far all the in-filling models I've found are only based on text inputs.
where you stitch two images together, one is the working image (the one you want to modify), and the other one is the reference image, you then instruct the model what to do. I'm guessing this approach is as brittle as the other attempts you've tried so far, but I thought this seemed like an interesting approach.
I don't think we can really answer the question if Flash will ever be released.
Most of my photos these days are 48MP and I don't want to lose a ton of resolution just to edit them.
// In this one, Gemini doesn't understand what "cinematic" is
"A cinematic underwater shot of a turtle gracefully swimming in crystal-clear water [...]"
// In this one, the reflection in the water in the background has different buildings
"A modern city where raindrops fall upward into the clouds instead of down, pedestrians calmly walking [...]"
Midjourney created both perfectly.
Editing models do not excel at aesthetic, but they can take your Midjourney image, adjust the composition, and make it perfect.
These types of models are the Adobe killer.
Midjourney wins on aesthetic for sure. Nothing else comes close. Midjourney images are just beautiful to behold.
David's ambition is to beat Google to building a world model you can play games in. He views the image and video business as a temporary intermediate to that end game.
Here's a comparison of Flux Dev, MJ, Imagen, and Flash 2.5.
https://genai-showdown.specr.net/?models=FLUX_1D%2CMIDJOURNE...
That being said, if image fidelity is absolutely paramount and/or your prompts are relatively simple - Midjourney can still be fun to experiment with particularly if you crank up the weirdness / chaos parameters.
https://developers.googleblog.com/en/introducing-gemini-2-5-...
It seems like this is 'nano-banana' all along
https://i.postimg.cc/dt7FsTWK/87-YE4j1-UC4x-Fp-Yo5t4wq-outpu...
I used this test site to benchmark them side by side. https://flux-krea.app/ Results? Comparable quality, but different strengths. Flux is better for precision edits and prompt adherence. Nano Banana shines in speed and creative blending. Pick your tool based on workflow, not hype.
That said, I think we’re still in the “GPT-3.5” phase of image editing: amazing compared to what came before, but still tripping over repeated patterns (keyboards, clocks, Go boards, hands) and sometimes refusing edits due to safety policies. The gap between hype demos and reproducible results is also very real; I’ve seen outputs veer from flawless to poor with just a tiny prompt tweak.
Sorry, there seems to be an error. Please try again soon.”
Never thought I would ever see this on a google owned websites!
Really? Google used to be famous not only for its errors, but for its creative error pages. I used to have a google.com bookmark that would send an animated 418.
Edit: OK, OK, I actually got it to work, and yes, I admit the results are incredible[2]. I honestly have no idea what happened with Pro 2.5 the first time.
[1]: https://g.co/gemini/share/5767894ee3bc [2]: https://g.co/gemini/share/a48c00eb6089
[1] https://developers.googleblog.com/en/introducing-gemini-2-5-...
I experimented heavily with 2.0 for a site I work on, but it never left preview and it had some gaps that were clearly due to being a small model (like lacking world knowledge, struggling with repetition, missing nuance in instructions, etc.)
2.5 Flash/nano-banana is a major step up but still has small model gaps peeking through. It still gets to "locked in" states where it's just repeating itself, which is a similar failure mode of small models for creative writing tasks.
A 2.5 Pro would likely close those gaps and definitively beat gpt-image-1
This is why I'm sticking mostly to Adobe Photoshop's AI editing because there are no restrictions in that regard.
I wasn't aware there is a channel where Google asks for feedback or where you are able to tell them that you love it. I only see a "report a problem button". Which channel is it?
It didn't succeed in doing the same recursively, but it's still clearly a huge advance in image models.
Also women are seen as more cooperative and submissive, hence so many home assistants and AI being women's voices/femme coded.
The way I see it - corporations would like to exploit prejudices for revenue. Of course, this is not something new. But it is a societal issue and the corporate world is playing a large role in it.
For context this was the original link - https://deepmind.google/models/gemini/image/
The response was a summary of the article that was pretty good, along with an image that dagnabbit, read the assignment.
1. Reduce article to a synopsis using an LLM
2. Generate 4-5 varying description prompts from the synopsis
3. Feed the prompts to an imagegen model
Though I'd wager that gpt-image-1 (in the ChatGPT) being multimodal could probably managed it as well.
Edit: the blog post is now loading and reports "1290 output tokens per image" even though on the AI studio it said something different.
To be honest I am kind of glad. As AI generated images proliferate, I am hoping it will be easier for humans to call them out as AI.
Flash Image is an image (and text) predicting large language model. In a similar fashion to how trained LLMs can manipulate/morph text, this can do that for images as well. Things like style transfer, character consistency etc.
You can communicate with it in a way you can't for imagen, and it has a better overall world understanding.
Gemini Flash Image: ChatGPT image, but by Google
Definitely inferior to results I see on AI Studio and image generation time is 6s on AI Studio vs 30 seconds on Fal.AI
Quality or latency?
Hope they get API issues resolved soon.
its only a matter of time before this can be used to generate coherence with nudity on consumer hardware.
https://9to5google.com/2025/08/25/android-apps-developer-ver...
Edit: Nevermind its not in gemini for everyone yet, its in aistudio though
And then call the next model vim-banana and start the editor-banana wars.
In all seriousness though, I'm seeing a worrying trend where google is hijacking well-known project names from other domains more and more now, with the "accidental"(?) side-effect of diluting their searchability and discoverability online, to the point I can no longer believe it is mere coincidence (whether malicious or not is another story altogether of course, but even if not, this is still a worrying trend).
I mean, what's next? Gopher 2.5 GIMP Video aka sublime-apple?
made me realize that AI image modification is now technically flawless, utterly devoid of taste, and that I myself am a rather unattractive fellow.
Given that this has been a serious problem with Google models, I would guess it would have been a good thing to at least add one such example in the marketing material. If the marketing material is to be believed, the model really prefers black females.
It hasn't been though, has it?
At one point one of their earliest image gen models had a prompting problem: they tried to have the LLM doing prompt expansion avoid always generating white people, since they realized white people were significantly overrepresented in their training data compared to their proportion of the population.
Unfortunately that prompt expansion would sometimes clash with cases where there was a specific race required for historical accuracy.
AFAIK they fixed that ages ago and it stopped being an issue.