Yeah, I've been mystified for years why they can't just train the "AI look" out of images. Is it, like, something undetectable by machine? But then how come we can often detect it so easily?
It's the opposite: the average person likes the AI look of images (the more "aesthetic" look) and RLHF keep converging toward that.
It is entirely possible to prompt engineer away from that style, but that requires effort and knowledge that said average person does not have, so it's stuck in a feedback loop.
I've been seeing "AI slop" stickers posted on things
> I see just everywhere including food menus
The places I have seen it in use are the places that would have a handwritten scribble that was misspelt and would be something mocked by people because it was written by ESOL (or vice versa, English speakers trying, but failing, to write in a different language)
Honestly, I think the AI slop is a step up - I can read the signs clearly, and they are only mildly offensive
Edit: I guess I take the view that - like a pirated movie filmed in a cinema - the only people that use/watch it are people that were never going to pay for the real thing anyway
I see restaurants post AI flyers and ads to random local Facebook groups and most of the replies are about them being AI slop. I think it’s an uncanny valley problem: People don’t mind if you post a real photo of your food or a cartoon drawing of a pizza or a pig on a spit, but when it’s a weird fake photo with texture issues, it looks gross, especially when it’s food.
I think a lot of people who open restaurants probably aren’t extremely online, partly because it would be physically impossible with the amount of work they have to do, but they definitely pay attention -sometimes too much attention — to public online feedback.
I get why restaurants use the cartoon-looking slop. If you don't particularly care that it signals that you put no effort into your menu or ad or that your restaurant's menu has the same shitty aesthetic as all other slop, fine. It's a cartoon and I could understand someone thinking it looks ok.
I absolutely do not get the uncanny-valley images. Those all look gross. Besides not actually being representative of the dish, the images have all sorts of weird texture issues, look too glossy, and are just unappetizing. I cannot understand why a restaurant owner would want to put up an ad that has food that looks straight up gross.
This is not true. Think of the problems AI has with creating appetizing food imagery, for example. Images that every human annotator marks as unappealing with a high degree of consensus.
The problem is in the rewards, but it's more how they average across domains and contexts, so AI misapplies them. Actually, in human preference evaluations, most of Gen Z in particular extremely adverse to the common look of AI images.
So it's not the annotators involved in RLHF, it's the models failing to map to human preferences at the task level. The failure rate is also uneven across domains.
Where there are challenges with human annotation, it's not the ability to gauge what's good but rather how to correct what's wrong. Most people can tell when something is off but struggle to articulate why. That's where more domain expertise is required.
>This is not true. Think of the problems AI has with creating appetizing food imagery, for example. Images that every human annotator marks as unappealing with a high degree of consensus.
I think the way they'll solve it in the end is to create an ultra-high-fidelity simulation of the world including modelling taste and other senses on the level of physics, and then incorporate all of this into the training rewards.
I think this is how the machines know what tasty wheat tasted like.
I think this is fundamental. People will probably dislike whatever it is that AI images look like, because whatever they look like is going to be associated with cheap. They could have a totally different aesthetic and I think most people's conception of cheap would adapt to whatever that is.
Some of it has to do with Classifier Free Guidance (CFG), a math thing that makes it more likely for a diffusion model to follow what you ask for in the prompt. However, turning up CFG inevitably causes high contrast and certain other artifacts.
With that being said, my understanding of that issue is a few years old, people have always said CFG is a havk until models get better at following prompts on their own. So thee “look” that tools like Nano Banana ends up with could be something else. It may or may not be a necessary artifact of some part of the process.
The problem isn't the classifier free guidance itself, but the choice of parameters. When a parameter above 1 is chosen you get images which very stronger conform to theme, but which there's not a good mathematical justification for.
You are actually a very skilled pattern recognizing machine. There's likely always going to be some bias and purple are going to be able to detect it and the human style will move away from it.
It does look terrible. But keep in mind these decisions aren't made to please you or me on HN. The numbers clearly show the average user does not care about the AI look because they can't really tell the difference between human-gen and AI-gen. Whatever saves them money is what they will go with.
> The numbers clearly show the average user does not care about the AI look because they can't really tell the difference between human-gen and AI-gen.
Do you have a link to the numbers that clearly show this?
Someone posted an AI picture on Twitter, that was done in the style of a great classic painter of renown. Everyone tore it apart, discussing all the details that betrayed it as a pale imitation of the original. It was roundly panned as an example of how AI will never be good enough to replace humans. After this went on for some time, the initial poster revealed it wasn't AI at all, it was actually a genuine original from the artist in question.
People just hate AI and will find a post hoc justification for their hatred in every situation. The link you gave could have just as easily been someone expressing surprise at the artists choice to not follow traditional physics... but instead be playful in unconstrained ways. Shrug.
I doubt it, same with people that use ChatGPT for everything and don’t see the slip anymore and are confused when you point it out. LLM blindness is a real thing.
I'm deeply interested in this subject. In conversation, most people I talk to say they hate the look of AI generated "art," or find it uncanny or uncomfortable. However, whenever I'm on the bus or train, if I peek over the shoulder of someone, it's 50% chance they're endlessly scrolling AI generated short form video, either as unrelated clips or Chinese dramas (this being in Taiwan).
I suspect people are somewhat hypnotized by the content, in the same way a toddler is hypnotized by Cocomelon. Something about it is enthralling and keeps the attention. I wonder if it's related to the phone game icon / YouTube thumbnail phenomenon of "person's face making an extreme expression with the mouth open." AI generated videos for example still tend to have very exaggerated facial expressions and the mouth is always unusually wide when talking. I also wonder if something about the uncanny smoothness of movement (I assume caused by any form of transition defaulting to "linear") has a hypnotic effect.
I'm not sure how to look into this further. Elsagate is an interesting past example, and what I've learned so far I threw in a blog post a couple months back https://blog.calebjay.com/posts/psychic-attack/
Right now I'm looking into "Lula's army" and the AI generated content they used. I feel like there's something significant and perhaps even dangerous that could be developed bere.
whats incredible is when in their video they're talking about complexity, the cat's images have extra limbs in low and medium complexity. They could've easily generated more versions to get the clean versions. But it just doesn't seem like they care. Maybe this is what someone who is so used to using AI to move fast becomes: someone who drops the quality bar to 80%?
That stained glass whale thing makes me especially grumpy. When it scrolled onto the page, my first thought was "whoah, what a neat idea" and then it was _immediately_ followed by "what a terribly execution."
Like, I'm not even an artist, so I'm sure there's a million things I'm missing, but it's just so upsetting to look at. There's weird random reflections on things that shouldn't have reflections, the coloured light absolutely doesn't actually track through the stained glass onto any surface that makes any sense, light is coming from every which way, it's just... gross to look at for anything more than a quarter of a second.
And this is what they're using to hype people up for it. /shrug
Not really, you could imagine another technique that 3D models it without force but with raytracing, puts it in that scene and takes a picture, the result would look better. That could then be a starting point for trying to get the lighting an panels just right to make the best reflections.
When you forgo process the odds that you'll make good art drop to 0. Tool use is a much better path forward for self driving, artistic computer than big data. Images 2.6 only has use as a tool.
I've never understood why people have this viewpoint. If there is no preference for one property of a work (ie. lighting) to match reality just because another unrelated property (biology) doesn't, then surely the only possible scenarios remaining are an exact replica of the real world (where all properties match the real world) or an entirely arbitrary construction (where no properties should match reality). Fiction is predicated on the idea of "reality, but...". Saying that any single difference from reality should lead you to expect no similarities to reality at all, if followed, would collapse all fictions into pure arbitrary soup.
Long before generative AI, unpleasant nerds would poop on fun images by claiming they could "just tell" that they were altered in photoshop. Today I'm seeing a complaint that the path tracing from the multi-layered stained glass into the cloud simulation is not as accurate as a human would have painted by hand. And, that apparently makes us grumpy.
There have been unending examples of people liking an image until informed it was made by AI. Then, suddenly they see all sorts of fine-detail excuses to dislike it. And, of people calling out "obviously AI" problems in the works of human master artists when someone lies and tells them it was generated.
I'm not seeing any difference in the discussion here. The whale is a bit overly high-frequency. The owl is fine. The guy at the computer would be considered great if a human made it. The travel ad is bland. The balloon letters are fine.
None of these are "terrible eye sores". Some people don't like AI generated imagery as a process. Seeing generated images make them feel upset. That's reasonable. But, claiming these images in isolation are objectively awful regardless of their source is reactionary. If any of them were made by a person using Photoshop and/or Blender, they would be received with a resounding "Meh" or better.
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)
EDIT: Per documentation, there is an explicit `output_format: "svg"`, and I am indeed correct there is raster-to-vector in play:
> The API creates a reference image and uses it to produce the SVG. The quality setting controls that reference image. Decode data[0].b64_json as base64 and save the resulting UTF-8 XML bytes with a .svg extension.
OpenAI claims 50% faster generation with Images 2.6 but curiously price is the same as Image 2.5.
Yeh using sprites to make frames consistent (kinda tilting latent space towards a character consistency, zero-shot ~LoRA hack?) is old-school. I did this 3 years ago. https://j11y.io/dance/
I tried it with Sunburst with both gpt-5.5 and gpt-5.6-sol, and the results were nothing spectacular. It’s something you could already do in the ChatGPT interface by asking it to convert an existing image to an equivalent SVG.
Recraft is probably the only group I know of that had a native text-to-SVG generator pretty early on, but since it’s a closed model, I’m not familiar with what they were doing under the hood technically.
Coding related have had massive step-up improvements over time.
The same cannot be said for image generation. If anything, I swear it's gone backwards.
It peaked with very early midjourney (like 2022), maybe follow ups with stable diffusion, and then with some of the nano banana models for actual editing. But overall, very lackluster in comparison to other areas.
There have been a lot of strides in open-weight models, well beyond SDXL. Flux, back in August of 2024 was a game changer, finally allowing us to move past SDXL’s nuclear pile of comma-delimited keywords for prompting and allowing more natural language.
And this summer we got Ideogram V4 and Krea2, both of which are highly capable.
You can see how the open/closed models have advanced since SDXL in the Progress tab.
MJ is pretty out-of-date by this point though it can still be fun from an image space exploration perspective. It scored a rather pitiful 2 out of 15 on my bench back in the summer of 2025.
You could argue that progress has been relatively flat since Nano Banana Pro, but that's still less than a full year old (just barely), and that was the first model that could really fully maintain visual consistency across complex edits. The GPT ones are a little stylistically disappointing in ways, but they've still been making steady progress too. I don't think we can claim a peak just yet, even if it's been slower.
Coding flattened out like 9 months ago. In reality, people are getting dumber and lazier from over using them, so they feel like they're improving when they aren't really, outside some areas like RE.
In reality RE has only gotten better because they RLd it into the models because the NEED data, so now every kid is decomping proprietary softwate and sending them back great training data.
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.
ChatGPT Images (and some others like Nano Banana) are autoregressive models, and the text model generates the tokens in the same way which are then passed in the correct form for an image model to decode. That is how it can reason the prime numbers + Pokemon + art style conditional.
It is different from earlier image models which just use a simple text encoder and struggle with conditional logic if not explicitly trained for it.
I think it's more impressive that it managed so much coherence in a single image than anything else. Very few problems with each of the pokemon anatomically speaking. Mew's kind of a mess but the rest are pretty accurate.
That being said, the ukiyo-e artwork looks more like a standard colored pencil illustration, and it sort of tried to cheat by adding some vaguely Japanese natural scenery.
See this Kickstarter all the way back in 2013 for a great example of what Pokémon might actually look like as ukiyo-e woodblock prints.
I remember that Kickstarter. I follow the woodblock carver who worked with the artist. His name is David Bull and he has some great videos on woodblock carving on YouTube: https://m.youtube.com/@seseragistudio
That prompt seems almost deliberately obtuse. The natural phrasing would be “greying blonde hair”. If you described someone as having half-X, half-Y hair, I’d expect to see something like that image, too.
The input and output pricing per token remains the same between 2.5 and 2.6, although using their image pricing calculator shows a quadrupling of token usage at low and medium quality settings for 2.6. For e.g. at 1024x1024:
2.5 low, 196 tokens, $0.006
2.6 low, 823 tokens, $0.025
2.5 med, 439 tokens, $0.013
2.6 med, 1449 tokens, $0.043
Interestingly, higher quality settings show no change in token usage between the models.
Similar results for my workflow. And the Round art comes out significantly less round without prompt changes (more input tokens)
Setting (1024x1024) Tokens Cost/image Time
2.5 medium 439 $0.014 12.0 s
2.6 low 824 $0.025 6.7 s
2.6 medium 1,449 $0.044 7.9 s
2.6 high 1,758 $0.053 8.9 s
Decided to try out one of the new features in GPT Image 2.6: the ability to natively generate animated GIF files.
I didn't have high expectations, and I still think you can probably get better results by doing a First-Last-Frame using something like Minimax H3 but I was still rather surprised that it could do it at all.
Here are some of the results. The last one shows its ability to take an existing image and convert it into an animated GIF. It's a picture of my husky inside a snow globe.
That's a bit hyperbolic. The windmill one is honestly not terrible (slight flicker on the door).
Honestly, you have to think about the use case. GIFs are going to be little throwaway things that you're pasting into Discord or meme-ing.
Nobody's reaching for a GIF if they're looking for professional results.
It's a shame that the SVG output seems to be limited to static vector graphics only though - or rather I couldn't get it to produce anything with the <animate> attribute.
Everyone is highly sensitive to slop, and it still looks like slop.
If you actually want something that looks decent, it feels like tool use would at least help cover the obvious blunders. Building layers to composite would go a long way to removing some slop here.
Did a considerably worse job than Image Gen 2.0 on my standard test prompt.
"A macro close-up photograph of an old watchmaker's hands carefully replacing a tiny gear inside a vintage pocket watch. The watch mechanism is partially submerged in a shallow dish of clear water, causing visible refraction and light caustics across the brass gears. A single drop of water is falling from a pair of steel tweezers, captured mid-splash on the water's surface. Reflect the watchmaker's face, slightly distorted, in the curved glass of the watch face. Sharp focus throughout, natural window lighting from the left, shot on 100mm macro lens."
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 either be too simplistic in its approaches, or create outputs that are inherently broken in other cases.
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.
I don't use AI image generation anymore, but when I was fiddling with it I would generate dozens of images per idea, limited only by my patience. "The same thing but a little different" is the easiest feedback you can give anything.
Same, when I messed around with it to learn how the models worked I would generate batches of hundreds or thousands with slightly tweaked prompts and negative prompts to get a sense of how the inputs changed the outputs.
As an aside, there was a recent post about building in an emulator in an open model. I think it was Qwen 3.6. Actually, I think this type of hard-coded calculation would be very well suited to a small image model as seemingly trivial tasks like symmetry or moving text or placing a vector are quite difficult to do for models currently.
On a phone, if you share a gif, it creates a link to ChatGPT.com with a static image. You can also save the image to photos and share directly from there which feels like an unnecessary step.
It feels like a sleazy pattern and defeats the purpose of why I’d want to generate a gif in the first place, which to make clear is likely going to be to share with friends in a chat. I’m not interested in driving traffic to ChatGPT.com to impress my friends.
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've been running a comparative image benchmark site for years now (see my profile for the link), ever since SDXL back in 2023, and the answer really comes down to it depends. My benchmark focuses primarily on prompt adherence, but that's just one metric you can use. (fidelity, censorship, explorative, speed, features, etc are others)
In terms of overall state of the art, the two major contenders are probably Nano Banana Pro (Google) and the GPT Image 2 series (OpenAI).
For pure prompt adherency, OpenAI has consistently lead the pack since the original gpt-image-1 released back in March of 2025.... but its OpenAI's fidelity has always left something to be desired. Version 2 of the series has definitely closed the gap a bit.
You may notice that luxury brands are making stuff by hand now. It's already painfully uncool to use AI images, regardless of how "real" they look – because they're cheap
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.
If you define pixel art as just a vague overall look, then yes, it can do pixel art. If you define pixel art as being limited to a fixed color palette, like 16 or 256 colors and aligned to a square grid, then no, not without some manual post-processing.
It's not helped by the fact that a lot of people who are not artistically inclined will generate pixel art on these image generators at 1MP resolutions (far higher than traditional pixel art), such that even the casual observer can typically see the bad smearing and fringing in the final result.
There are SDXL pixel art models out there that do actual pixel art. They generate images that are 8x too large, you need to downscale them and reduce colors externally.
yeah, the original Retro Diffusion models were an early example of that - IIRC it had models separately fine-tuned for 32x32, 64x64, etc. so you'd nearest neighbor squash'em down to that size after the initial gen to kill the high frequency noise.
It looks like the images in there are 'based' on the "hot springs symbol" - a Japanese map symbol encoded into Unicode that seems to have become synonymous with "piping hot bread" in the West!
The images in the GPs link show a common pictogram for artisanal bread. The image itself is actually a close representation of the real thing. An image search for sourdough will return many images showing bread with a similar appearance.
Anecdotally, I’ve never seen that hot springs symbol used for bread, and I’ve lived in the west my whole life.
Not without irony, but connecting the bread symbol with the hot springs symbol feels like the sort of mistake an llm would make.
The only animation format it supports right now is GIF so you can only have a transparency channel that's either on or off. It doesn't support animated WebP.
Not sure what that says about grok, Claude, and runway flagging that and being unable to generate that, or me not having the prompting skills to do that without help.
I really don't like this generative AI. I may look at it like an artist but completed generated software, writing, music and imagery is a robbery of human worth. Absolutely.
No, it's created by data scanned from the internet, books and fed to the models. How is that a good thing for anyone? I'd love your take. People spend years giving themselves value to others to live a life of worth and value.
The data just scanned itself and organized itself into an neural network? Would be an awesome display of bootstrapped intelligence if that happened! Sadly, it was the result of decades of human research.
Still looks like an eyesore, I genuinely want to know if the engineers who work on the product don't notice it
Yeah, I've been mystified for years why they can't just train the "AI look" out of images. Is it, like, something undetectable by machine? But then how come we can often detect it so easily?
It's the opposite: the average person likes the AI look of images (the more "aesthetic" look) and RLHF keep converging toward that.
It is entirely possible to prompt engineer away from that style, but that requires effort and knowledge that said average person does not have, so it's stuck in a feedback loop.
I still can’t believe the amount of deepfakes and slop I see just everywhere including food menus and people don’t even seem to notice.
This kind of content is here to stay and we’re going to see more and more of it. Unfortunately…
> people don’t even seem to notice.
I've been seeing "AI slop" stickers posted on things
> I see just everywhere including food menus
The places I have seen it in use are the places that would have a handwritten scribble that was misspelt and would be something mocked by people because it was written by ESOL (or vice versa, English speakers trying, but failing, to write in a different language)
Honestly, I think the AI slop is a step up - I can read the signs clearly, and they are only mildly offensive
Edit: I guess I take the view that - like a pirated movie filmed in a cinema - the only people that use/watch it are people that were never going to pay for the real thing anyway
People definitely seem to notice.
I see restaurants post AI flyers and ads to random local Facebook groups and most of the replies are about them being AI slop. I think it’s an uncanny valley problem: People don’t mind if you post a real photo of your food or a cartoon drawing of a pizza or a pig on a spit, but when it’s a weird fake photo with texture issues, it looks gross, especially when it’s food.
I think a lot of people who open restaurants probably aren’t extremely online, partly because it would be physically impossible with the amount of work they have to do, but they definitely pay attention -sometimes too much attention — to public online feedback.
I get why restaurants use the cartoon-looking slop. If you don't particularly care that it signals that you put no effort into your menu or ad or that your restaurant's menu has the same shitty aesthetic as all other slop, fine. It's a cartoon and I could understand someone thinking it looks ok.
I absolutely do not get the uncanny-valley images. Those all look gross. Besides not actually being representative of the dish, the images have all sorts of weird texture issues, look too glossy, and are just unappetizing. I cannot understand why a restaurant owner would want to put up an ad that has food that looks straight up gross.
The saddest words of tongue or pen: “you’re not the target demographic”
Isn't this the sum of dating apps?
No, you are confusing a model with reality.
This is not true. Think of the problems AI has with creating appetizing food imagery, for example. Images that every human annotator marks as unappealing with a high degree of consensus.
The problem is in the rewards, but it's more how they average across domains and contexts, so AI misapplies them. Actually, in human preference evaluations, most of Gen Z in particular extremely adverse to the common look of AI images.
So it's not the annotators involved in RLHF, it's the models failing to map to human preferences at the task level. The failure rate is also uneven across domains.
Where there are challenges with human annotation, it's not the ability to gauge what's good but rather how to correct what's wrong. Most people can tell when something is off but struggle to articulate why. That's where more domain expertise is required.
> Think of the problems AI has with creating appetizing food imagery, for example.
For posterity, the memetic ugly food can be controlled and mostly filtered out with prompt engineering. Here's a quick ad hoc test example which (hopefully) uses the new model: https://chatgpt.com/share/6aebf030-9d38-83e8-a103-3509883905...
First image is fine, but attempts to make it more wacky turn it to the AI food slop. So then followup prompts deslopify it, mostly successfully.
tbh it got worse every time, the first one is the best lol
Eh, looks like slop still in my opinion. The evenness and density of the teeth especially stands out to me as uncanny.
>This is not true. Think of the problems AI has with creating appetizing food imagery, for example. Images that every human annotator marks as unappealing with a high degree of consensus.
I think the way they'll solve it in the end is to create an ultra-high-fidelity simulation of the world including modelling taste and other senses on the level of physics, and then incorporate all of this into the training rewards.
I think this is how the machines know what tasty wheat tasted like.
I think this is fundamental. People will probably dislike whatever it is that AI images look like, because whatever they look like is going to be associated with cheap. They could have a totally different aesthetic and I think most people's conception of cheap would adapt to whatever that is.
for the same reason they can't train llmisms out of text.
Some of it has to do with Classifier Free Guidance (CFG), a math thing that makes it more likely for a diffusion model to follow what you ask for in the prompt. However, turning up CFG inevitably causes high contrast and certain other artifacts.
With that being said, my understanding of that issue is a few years old, people have always said CFG is a havk until models get better at following prompts on their own. So thee “look” that tools like Nano Banana ends up with could be something else. It may or may not be a necessary artifact of some part of the process.
The problem isn't the classifier free guidance itself, but the choice of parameters. When a parameter above 1 is chosen you get images which very stronger conform to theme, but which there's not a good mathematical justification for.
You are actually a very skilled pattern recognizing machine. There's likely always going to be some bias and purple are going to be able to detect it and the human style will move away from it.
It does look terrible. But keep in mind these decisions aren't made to please you or me on HN. The numbers clearly show the average user does not care about the AI look because they can't really tell the difference between human-gen and AI-gen. Whatever saves them money is what they will go with.
It's not like it's saving people money. Instead it's letting them create things they never could have before
> The numbers clearly show the average user does not care about the AI look because they can't really tell the difference between human-gen and AI-gen.
Do you have a link to the numbers that clearly show this?
The numbers of AI posters/signs/ads I see on the street…
That stained glass sky-whale, at the bottom of the page, is pretty nice.
and yet https://news.ycombinator.com/item?id=50372945#50373586
Someone posted an AI picture on Twitter, that was done in the style of a great classic painter of renown. Everyone tore it apart, discussing all the details that betrayed it as a pale imitation of the original. It was roundly panned as an example of how AI will never be good enough to replace humans. After this went on for some time, the initial poster revealed it wasn't AI at all, it was actually a genuine original from the artist in question.
People just hate AI and will find a post hoc justification for their hatred in every situation. The link you gave could have just as easily been someone expressing surprise at the artists choice to not follow traditional physics... but instead be playful in unconstrained ways. Shrug.
I doubt it, same with people that use ChatGPT for everything and don’t see the slip anymore and are confused when you point it out. LLM blindness is a real thing.
This. You shoulda seen OpenAI’s own cookbook for GPT-Image-2 - literally their own prompting guide - and the dog comic was a complete mess.
Generative AI (coding, images, etc.) has only accelerated the atrophy of basic observational skills.
I'm deeply interested in this subject. In conversation, most people I talk to say they hate the look of AI generated "art," or find it uncanny or uncomfortable. However, whenever I'm on the bus or train, if I peek over the shoulder of someone, it's 50% chance they're endlessly scrolling AI generated short form video, either as unrelated clips or Chinese dramas (this being in Taiwan).
I suspect people are somewhat hypnotized by the content, in the same way a toddler is hypnotized by Cocomelon. Something about it is enthralling and keeps the attention. I wonder if it's related to the phone game icon / YouTube thumbnail phenomenon of "person's face making an extreme expression with the mouth open." AI generated videos for example still tend to have very exaggerated facial expressions and the mouth is always unusually wide when talking. I also wonder if something about the uncanny smoothness of movement (I assume caused by any form of transition defaulting to "linear") has a hypnotic effect.
I'm not sure how to look into this further. Elsagate is an interesting past example, and what I've learned so far I threw in a blog post a couple months back https://blog.calebjay.com/posts/psychic-attack/
Right now I'm looking into "Lula's army" and the AI generated content they used. I feel like there's something significant and perhaps even dangerous that could be developed bere.
whats incredible is when in their video they're talking about complexity, the cat's images have extra limbs in low and medium complexity. They could've easily generated more versions to get the clean versions. But it just doesn't seem like they care. Maybe this is what someone who is so used to using AI to move fast becomes: someone who drops the quality bar to 80%?
That stained glass whale thing makes me especially grumpy. When it scrolled onto the page, my first thought was "whoah, what a neat idea" and then it was _immediately_ followed by "what a terribly execution."
Like, I'm not even an artist, so I'm sure there's a million things I'm missing, but it's just so upsetting to look at. There's weird random reflections on things that shouldn't have reflections, the coloured light absolutely doesn't actually track through the stained glass onto any surface that makes any sense, light is coming from every which way, it's just... gross to look at for anything more than a quarter of a second.
And this is what they're using to hype people up for it. /shrug
> There's weird random reflections on things that shouldn't have reflections
It's an image depicting a whale made of stained glass; realism is off the table there.
Well no. I can imagine lots of things in my head that are impossible but would be correctly lit, and I could do the same in Blender.
Not really, you could imagine another technique that 3D models it without force but with raytracing, puts it in that scene and takes a picture, the result would look better. That could then be a starting point for trying to get the lighting an panels just right to make the best reflections.
When you forgo process the odds that you'll make good art drop to 0. Tool use is a much better path forward for self driving, artistic computer than big data. Images 2.6 only has use as a tool.
I've never understood why people have this viewpoint. If there is no preference for one property of a work (ie. lighting) to match reality just because another unrelated property (biology) doesn't, then surely the only possible scenarios remaining are an exact replica of the real world (where all properties match the real world) or an entirely arbitrary construction (where no properties should match reality). Fiction is predicated on the idea of "reality, but...". Saying that any single difference from reality should lead you to expect no similarities to reality at all, if followed, would collapse all fictions into pure arbitrary soup.
nah, the physical light is still a real concept, and it looks really shitty here.
Unpleasable nerds with “I can tell by the pixels” ass comments is what keeps me coming back to HN year after year
Just an absolute race to post a contrarian take to hit the top of the comments.
Never change bros
If not by the pixels, how else could you assess the quality of an image?
Vibes?
It's a reference to the very old https://knowyourmeme.com/memes/this-looks-shopped
Long before generative AI, unpleasant nerds would poop on fun images by claiming they could "just tell" that they were altered in photoshop. Today I'm seeing a complaint that the path tracing from the multi-layered stained glass into the cloud simulation is not as accurate as a human would have painted by hand. And, that apparently makes us grumpy.
There have been unending examples of people liking an image until informed it was made by AI. Then, suddenly they see all sorts of fine-detail excuses to dislike it. And, of people calling out "obviously AI" problems in the works of human master artists when someone lies and tells them it was generated.
I'm not seeing any difference in the discussion here. The whale is a bit overly high-frequency. The owl is fine. The guy at the computer would be considered great if a human made it. The travel ad is bland. The balloon letters are fine.
None of these are "terrible eye sores". Some people don't like AI generated imagery as a process. Seeing generated images make them feel upset. That's reasonable. But, claiming these images in isolation are objectively awful regardless of their source is reactionary. If any of them were made by a person using Photoshop and/or Blender, they would be received with a resounding "Meh" or better.
> Please don't fulminate. Please don't sneer, including at the rest of the community.
> Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith.
> Eschew flamebait. Avoid generic tangents. Omit internet tropes.
https://news.ycombinator.com/newsguidelines.html
thanks bro I would have never known
Tell that to all the top level comments. Just a bunch of shallow reddit snark.
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)
EDIT: Per documentation, there is an explicit `output_format: "svg"`, and I am indeed correct there is raster-to-vector in play:
> The API creates a reference image and uses it to produce the SVG. The quality setting controls that reference image. Decode data[0].b64_json as base64 and save the resulting UTF-8 XML bytes with a .svg extension.
OpenAI claims 50% faster generation with Images 2.6 but curiously price is the same as Image 2.5.
Image 2.6 costs more tokens for generating low and medium quality images, so it ends up being a bit more expensive. Could refer to https://developers.openai.com/api/docs/guides/image-generati...
Yeh using sprites to make frames consistent (kinda tilting latent space towards a character consistency, zero-shot ~LoRA hack?) is old-school. I did this 3 years ago. https://j11y.io/dance/
From what I can tell after some preliminary testing, you have to pass an additional large language model to the OpenAI API when generating SVGs.
I couldn't find this requirement in the documentation, but if you don't pass one in, you get this error:
I tried it with Sunburst with both gpt-5.5 and gpt-5.6-sol, and the results were nothing spectacular. It’s something you could already do in the ChatGPT interface by asking it to convert an existing image to an equivalent SVG.
https://imgpb.com/bkUrc
Recraft is probably the only group I know of that had a native text-to-SVG generator pretty early on, but since it’s a closed model, I’m not familiar with what they were doing under the hood technically.
https://fal.ai/models/fal-ai/recraft/v4/pro/text-to-vector
Just wasted 4hrs on the model by trying to do consistent sprite sheet art.
Nothing to see here.
Feels like ai image generation has plateaud.
Coding related have had massive step-up improvements over time.
The same cannot be said for image generation. If anything, I swear it's gone backwards.
It peaked with very early midjourney (like 2022), maybe follow ups with stable diffusion, and then with some of the nano banana models for actual editing. But overall, very lackluster in comparison to other areas.
There have been a lot of strides in open-weight models, well beyond SDXL. Flux, back in August of 2024 was a game changer, finally allowing us to move past SDXL’s nuclear pile of comma-delimited keywords for prompting and allowing more natural language.
And this summer we got Ideogram V4 and Krea2, both of which are highly capable. You can see how the open/closed models have advanced since SDXL in the Progress tab.
https://genai-showdown.specr.net
MJ is pretty out-of-date by this point though it can still be fun from an image space exploration perspective. It scored a rather pitiful 2 out of 15 on my bench back in the summer of 2025.
There’s less money to be made with images, than code gen.
Investment in new models follows the money.
You could argue that progress has been relatively flat since Nano Banana Pro, but that's still less than a full year old (just barely), and that was the first model that could really fully maintain visual consistency across complex edits. The GPT ones are a little stylistically disappointing in ways, but they've still been making steady progress too. I don't think we can claim a peak just yet, even if it's been slower.
Coding flattened out like 9 months ago. In reality, people are getting dumber and lazier from over using them, so they feel like they're improving when they aren't really, outside some areas like RE.
In reality RE has only gotten better because they RLd it into the models because the NEED data, so now every kid is decomping proprietary softwate and sending them back great training data.
>Coding flattened out like 9 months ago.
It absolutely has not. You do have a nice point with data though, somehow haven't thought about that
Seedream 4 continues to be the best model by far at character consistency. I havnt done my tests on this model yet, however.
https://chatgpt.com/share/6aebc6f8-d660-83e8-9cb3-49bdf8caa7...
I guess I should have been careful about “half”?
These models have a hard time imagining men with naturally blond hair getting grey.
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.
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...
Wow. Are those the correct Pokemon and everything? Quite cool.
Yes, the Pokemon are correct. Compare to my attempt a year ago with Nano Banana Pro, which plagiarized the images, didn't do interesting things with the background, and got 271 wrong: https://minimaxir.com/2025/12/nano-banana-pro/pokemongrid_hu...
When I did a similar prompt with ChatGPT 2.0 it was worse, so interesting to see improvement.
On the back end a language model is effectively writing a long detailed prompt describing this image exactly right? The image model can't know this.
ChatGPT Images (and some others like Nano Banana) are autoregressive models, and the text model generates the tokens in the same way which are then passed in the correct form for an image model to decode. That is how it can reason the prime numbers + Pokemon + art style conditional.
It is different from earlier image models which just use a simple text encoder and struggle with conditional logic if not explicitly trained for it.
Yes it thinks about it and then calls the image tool with the detailed prompt with exactly what to include in the image
I think it's more impressive that it managed so much coherence in a single image than anything else. Very few problems with each of the pokemon anatomically speaking. Mew's kind of a mess but the rest are pretty accurate.
That being said, the ukiyo-e artwork looks more like a standard colored pencil illustration, and it sort of tried to cheat by adding some vaguely Japanese natural scenery.
See this Kickstarter all the way back in 2013 for a great example of what Pokémon might actually look like as ukiyo-e woodblock prints.
https://www.kickstarter.com/projects/1499165518/ukiyo-e-hero...
https://ricedigital.co.uk/this-japanese-ukiyo-e-pokemon-art-...
I remember that Kickstarter. I follow the woodblock carver who worked with the artist. His name is David Bull and he has some great videos on woodblock carving on YouTube: https://m.youtube.com/@seseragistudio
Oh, thanks for showing me these videos! The way that he presents is so calm - he's like a woodprint Bob Ross.
That prompt seems almost deliberately obtuse. The natural phrasing would be “greying blonde hair”. If you described someone as having half-X, half-Y hair, I’d expect to see something like that image, too.
The input and output pricing per token remains the same between 2.5 and 2.6, although using their image pricing calculator shows a quadrupling of token usage at low and medium quality settings for 2.6. For e.g. at 1024x1024:
2.5 low, 196 tokens, $0.006
2.6 low, 823 tokens, $0.025
2.5 med, 439 tokens, $0.013
2.6 med, 1449 tokens, $0.043
Interestingly, higher quality settings show no change in token usage between the models.
Similar results for my workflow. And the Round art comes out significantly less round without prompt changes (more input tokens)
same in my testing; super deceptive marketing here. it’s 3-4x the true cost of 2.5.
Output image tokens per image (2.5 Flare → 2.6 Flare / Sunburst)
low, 1024x1024: 196 → 824 (4.2x) medium, 1024x1024: 439 → 1,449 (3.3x) low, 1024x1536: 158 → 664 (4.2x) medium, 1024x1536: 343 → 1,132 (3.3x)
Same $30/1M output tokens on all three, so per image: low: $0.006 → $0.025 medium: $0.013 → $0.043
Ask HN: Does anyone know whether a Codex subscription can generate images specifying gpt-image-2.6-sunburst without relying on an api_key?
The model used by codex-cli is still gpt-image-2. https://github.com/openai/codex/blob/f3074e35c079bd8a5e89b03...
Decided to try out one of the new features in GPT Image 2.6: the ability to natively generate animated GIF files.
I didn't have high expectations, and I still think you can probably get better results by doing a First-Last-Frame using something like Minimax H3 but I was still rather surprised that it could do it at all.
Here are some of the results. The last one shows its ability to take an existing image and convert it into an animated GIF. It's a picture of my husky inside a snow globe.
https://imgpb.com/OzSMKJ
The splotchiness on that simple chess animation makes this GIF feature a nonstarter.
Yeahhhh but that’s like saying Dall-E isn’t good enough
Ain’t wrong buuuuut…
That's a bit hyperbolic. The windmill one is honestly not terrible (slight flicker on the door).
Honestly, you have to think about the use case. GIFs are going to be little throwaway things that you're pasting into Discord or meme-ing.
Nobody's reaching for a GIF if they're looking for professional results.
It's a shame that the SVG output seems to be limited to static vector graphics only though - or rather I couldn't get it to produce anything with the <animate> attribute.
Everyone is highly sensitive to slop, and it still looks like slop.
If you actually want something that looks decent, it feels like tool use would at least help cover the obvious blunders. Building layers to composite would go a long way to removing some slop here.
Off-topic, but the looping GIFs of stock prices going up / going down remind me of the currently airing anime https://en.wikipedia.org/wiki/FX_Fighter_Kurumi-chan .
Did a considerably worse job than Image Gen 2.0 on my standard test prompt.
"A macro close-up photograph of an old watchmaker's hands carefully replacing a tiny gear inside a vintage pocket watch. The watch mechanism is partially submerged in a shallow dish of clear water, causing visible refraction and light caustics across the brass gears. A single drop of water is falling from a pair of steel tweezers, captured mid-splash on the water's surface. Reflect the watchmaker's face, slightly distorted, in the curved glass of the watch face. Sharp focus throughout, natural window lighting from the left, shot on 100mm macro lens."
2.6: https://gov.f9.is/JRDpHJGu
2.0: https://gov.f9.is/cqyCwtq9
tweezers not holding the gear, and THOSE FINGERS, yick. What else?
Whatever site you are hosting images seems to trap my mobile browser and require 3-4 back buttons to get out of.
Hmm, it's jumpshare one of those drag and drop file share companies (that I pay for). Thanks for the heads up I'll open a support ticket.
2.5 is better than 2.6
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 either be too simplistic in its approaches, or create outputs that are inherently broken in other cases.
Comparison examples: https://diffui.ai/app/canvas/178db5d5-ff87-4b43-aa98-2897773...
2.6-sunburst seems solid however.
Update: seems to be confirmed on arena.ai - 2.5 scores higher than 2.6 https://arena.ai/leaderboard/image/text-to-image
I preferred 2.6 in all examples supplied!
I firmly disagree. There's subjective comparison, and there's objective comparison.
In two of the examples the images from 2.6 are inherently broken, ie: https://image.non.io/161d8f02-fc2a-40e0-91b2-9d27ceb8fef1.we...
Objectively there are tests here where 2.5 are better. Subjectively prefering the layout of 2.6 is fine, but there are blocking flaws.
Update: based on info from design arena, it looks like gpt-image-2.6 was post-trained to output less text: https://x.com/DesignArena/status/2118124381155303918
This may be why text elements in particular appear more "zoomed in", and why it's providing less detail for UI design.
Oh thank god, the awful inspirational text shoved needlessly into everything has been excised.
FOUR BILLION IMAGES PER WEEK???
How depressing.
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.
I don't use AI image generation anymore, but when I was fiddling with it I would generate dozens of images per idea, limited only by my patience. "The same thing but a little different" is the easiest feedback you can give anything.
Same, when I messed around with it to learn how the models worked I would generate batches of hundreds or thousands with slightly tweaked prompts and negative prompts to get a sense of how the inputs changed the outputs.
The blog post says
> Images 2.6 is available globally today in ChatGPT and Codex across all subscription tiers on desktop, mobile, and web.
But I just updated my codex-cli to the latest version (still 0.166.0) and it is still unable to generate gifs. Very disappointing
As an aside, there was a recent post about building in an emulator in an open model. I think it was Qwen 3.6. Actually, I think this type of hard-coded calculation would be very well suited to a small image model as seemingly trivial tasks like symmetry or moving text or placing a vector are quite difficult to do for models currently.
Thats why it became completely incompetent today trying to generate an image! I love half baked "test it in production" updates...
On a phone, if you share a gif, it creates a link to ChatGPT.com with a static image. You can also save the image to photos and share directly from there which feels like an unnecessary step.
It feels like a sleazy pattern and defeats the purpose of why I’d want to generate a gif in the first place, which to make clear is likely going to be to share with friends in a chat. I’m not interested in driving traffic to ChatGPT.com to impress my friends.
But then Meta/Apple/Google gets your behavioral surplus for selling Ads instead of OpenAI. Do you want higher subscription costs?
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.
How do you sensibly benchmark this product?
https://openrouter.ai/benchmarks/media/images
Depends on the use. For pics, nano banana still feels like the best. For designs/posters, gpt-image is king by a very large margin.
Generally depends on use case and style. Midjourney, Seedream, GPT Image are commonly regarded contenders IMO.
I've been running a comparative image benchmark site for years now (see my profile for the link), ever since SDXL back in 2023, and the answer really comes down to it depends. My benchmark focuses primarily on prompt adherence, but that's just one metric you can use. (fidelity, censorship, explorative, speed, features, etc are others)
In terms of overall state of the art, the two major contenders are probably Nano Banana Pro (Google) and the GPT Image 2 series (OpenAI).
For pure prompt adherency, OpenAI has consistently lead the pack since the original gpt-image-1 released back in March of 2025.... but its OpenAI's fidelity has always left something to be desired. Version 2 of the series has definitely closed the gap a bit.
That has to be a "draw the rest of the fucking owl" reference in the middle there, right?
https://imgur.com/how-i-feel-when-somebody-gives-me-advice-g...
My only use case for this is animating custom Slack emojis.
But trust, I'm a have a field day with that.
"we have Steve Aoki at home"
You may notice that luxury brands are making stuff by hand now. It's already painfully uncool to use AI images, regardless of how "real" they look – because they're cheap
SVG? Who can generate a pelican on a bicycle?
Can it finally do pixel art?
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.
If you define pixel art as just a vague overall look, then yes, it can do pixel art. If you define pixel art as being limited to a fixed color palette, like 16 or 256 colors and aligned to a square grid, then no, not without some manual post-processing.
It's not helped by the fact that a lot of people who are not artistically inclined will generate pixel art on these image generators at 1MP resolutions (far higher than traditional pixel art), such that even the casual observer can typically see the bad smearing and fringing in the final result.
There are SDXL pixel art models out there that do actual pixel art. They generate images that are 8x too large, you need to downscale them and reduce colors externally.
yeah, the original Retro Diffusion models were an early example of that - IIRC it had models separately fine-tuned for 32x32, 64x64, etc. so you'd nearest neighbor squash'em down to that size after the initial gen to kill the high frequency noise.
wait... did we get (sort of) sora back???
Video generation models work much differently than what this GIF pipeline is doing here.
"keep the shallow depth of field"
adds more depth of field
Stop, stop. AI is dead. Bailouts aren't coming.
The gifs really accentuate the artifacting, not sure I would've lead with that.
Do they have an example of an SVG image created by the new model somewhere?
Documentation has two: https://developers.openai.com/api/docs/guides/image-generati...
It looks like the images in there are 'based' on the "hot springs symbol" - a Japanese map symbol encoded into Unicode that seems to have become synonymous with "piping hot bread" in the West!
https://emojipedia.org/hot-springs
I always thought it was coffee like the Java logo lol
The images in the GPs link show a common pictogram for artisanal bread. The image itself is actually a close representation of the real thing. An image search for sourdough will return many images showing bread with a similar appearance. Anecdotally, I’ve never seen that hot springs symbol used for bread, and I’ve lived in the west my whole life. Not without irony, but connecting the bread symbol with the hot springs symbol feels like the sort of mistake an llm would make.
Still has the gross artifacts (visible watermarking) that the other GPT-Image-2.* has. Google's Nano banana watermarking is practically invisible.
SVG is cool though, I wonder how good it is.
Can we do animated sprites with alpha channel yet?
The only animation format it supports right now is GIF so you can only have a transparency channel that's either on or off. It doesn't support animated WebP.
See the second image of the bird flying.
https://imgpb.com/OzSMKJ
lol, I tried
> Create a GIF where xkcd 2347 gets hit by an airliner like the twin towers and the pilot is AI
and got a refusal.
> We’re so sorry, but the image we created may violate our guardrails around violence.
“Is the topic and concept from the original clear?“
Follow by
“Make it”
Seems to resolve the refusals
Wow. Thanks!
https://solyin.com/dependency-ai.gif
Not sure what that says about grok, Claude, and runway flagging that and being unable to generate that, or me not having the prompting skills to do that without help.
...I tried a similar prompt today and it refused
Nice, but they still haven't reopened two of my closed accounts. They're the only service I can't use.
anyone else get the feeling this is a chinese model they are just hosting themselves?
I really don't like this generative AI. I may look at it like an artist but completed generated software, writing, music and imagery is a robbery of human worth. Absolutely.
Generative AI is created by human worth.
No, it's created by data scanned from the internet, books and fed to the models. How is that a good thing for anyone? I'd love your take. People spend years giving themselves value to others to live a life of worth and value.
The data just scanned itself and organized itself into an neural network? Would be an awesome display of bootstrapped intelligence if that happened! Sadly, it was the result of decades of human research.
Yep, that's the sad part.
Human wealth? Humans, some, are worth more than others, per birth rates and varying investments by institutions.