HN Reader

https://mordenstar.com/blog/chatgpt-4o-images

It's definitely impressive though once again fell flat on the ability to render a 9-pointed star.

Example: Ask it to draw a notepad with an empty tic-tac-toe, then tell it to make the first move, then you make a move, and so on.

You can also do very impressive information-conserving translations, such as changing the drawing style, but also stuff like "change day to night", or "put a hat on him", and so forth.

I get the feeling these models are quite restricted in resolution, and that more work in this space will let us do really wild things such as ask a model to create an app step by step first completely in images, essentially designing the whole app with text and all, then writing the code to reproduce it. And it also means that a model can take over from a really good diffusion model, so even if the original generations are not good, it can continue "reasoning" on an external image.

Finally, once these models become faster, you can imagine a truly generative UI, where the model produces the next frame of the app you are using based on events sent to the LLM (which can do all the normal things like using tools, thinking, etc). However, I also believe that diffusion models can do some of this, in a much faster way.

Then google:

> Gemini 2.5: Our most intelligent AI model

> Introducing Gemini 2.0 | Our most capable AI model yet

I could go on forever. I hope this trend dies and apple starts using something effective so all the other companies can start copying a new lexicon.

No API yet, and given the slowness I imagine it will cost much more than the $0.03+/image of competitors.

Currently, my prompts seem to be going to the latter still, based on e.g. my source image being very obviously looped through a verbal image description and back to an image, compared to gemini-2.0-flash-exp-image-generation. A friend with a Plus plan has been getting responses from either.

The long-term plan seems to be to move to 4o completely and move Dall-E to its own tab, though, so maybe that problem will resolve itself before too long.

I'm not saying that it's not true, it's just "wait and see" before you take their word as gold.

I think MS's claim on their quantum computing breakthrough is the latest form of this.

Was anyone else surprised how slow the images were to generate in the livestream? This seems notably slower than DALLE.

I was blown away when they showed this many months ago, and found it strange that more people weren't talking about it.

This is much more precise than the Gemini one that just came out recently.

To think that a few years ago we had dreamy pictures with eyes everywhere. And not long ago we were always identifying the AI images by the 6 fingered people.

I wonder how well the physics is modeled internally. E.g. if you prompt it to model some difficult ray tracing scenario (a box with a separating wall and a light in one of the chambers which leaks through to the other chamber etc)?

Or if you have a reflective chrome ball in your scene, how well does it understand that the image reflected must be an exact projection of the visible environment?

How much longer until an AI that can generate 30 frames with this quality and make a movie?

About 1.5 years ago, I thought AI would eventually allow anyone with an idea to make a Hollywood quality movie. Seems like we're not too far off. Maybe 2-3 more years?

For example, I asked it to render a few lines of text on a medieval scroll, and it basically looked like a picture of a gothic font written onto a background image of a scroll

Asking it to draw the Balkans map in Tolkien style, this is actually really impressive, geography is more or less completely correct, borders and country locations are wrong, but it feels like something I could get it to fix.

Generate a photo of a lake taken by a mobile phone camera. No hands or phones in the photo, just the lake.

The hand holding a phone is always there :D

Theme: Educational Scientific Visualization – Ultra Realistic Cutaways Color: Naturalistic palettes that reflect real-world materials (e.g., rocky grays, soil browns, fiery reds, translucent biological tones) with high contrast between layers for clarity Camera: High-resolution macro and sectional views using a tilt-shift camera for extreme detail; fixed side angles or dynamic isometric perspective to maximize spatial understanding Film Stock: Hyper-realistic digital rendering with photogrammetry textures and 8K fidelity, simulating studio-grade scientific documentation Lighting: Studio-quality three-point lighting with soft shadows and controlled specular highlights to reveal texture and depth without visual noise Vibe: Immersive and precise, evoking awe and fascination with the inner workings of complex systems; blends realism with didactic clarity Content Transformation: The input is transformed into a hyper-detailed, realistically textured cutaway model of a physical or biological structure—faithful to material properties and scale—enhanced for educational use with visual emphasis on internal mechanics, fluid systems, and spatial orientation

Examples: 1. A photorealistic geological cutaway of Earth showing crust, tectonic plates, mantle convection currents, and the liquid iron core with temperature gradients and seismic wave paths. 2. An ultra-detailed anatomical cross-section of the human torso revealing realistic organs, vasculature, muscular layers, and tissue textures in lifelike coloration. 3. A high-resolution cutaway of a jet engine mid-operation, displaying fuel flow, turbine rotation, air compression zones, and combustion chamber intricacies. 4. A hyper-realistic underground slice of a city showing subway lines, sewage systems, electrical conduits, geological strata, and building foundations. 5. A realistic cutaway of a honeybee hive with detailed comb structures, developing larvae, worker bee behavior zones, and active pollen storage processes.

One area where it does not work well at all is modifying photographs of people's faces.* Completely fumbles if you take a selfie and ask it to modify your shirt, for example.

* = unless the people are in the training set

The glaring issue for the older image generators is how it would proudly proclaim to have presented an image with a description that has almost no relation to the image it actually provided.

I'm not sure if this update improves on this aspect. It may create the illusion of awareness of the picture by having better prompt adherence.

It's much better than prior models, but still generates hands with too many fingers, bodies with too many arms, etc.

https://news.ycombinator.com/item?id=42628742

The new one can.

https://chatgpt.com/share/67e36dee-6694-8010-b337-04f37eeb5c...

> Developers will soon be able to generate images with GPT‑4o via the API, with access rolling out in the next few weeks.

That's it folks. Tens of thousands of so-called "AI" image generator startups have been obliterated and taking digital artists with them all reduced to near zero.

Now you have a widely accessible meme generator with the name "ChatGPT".

The last task is for an open weight model that competes against this and is faster and all for free.

Trying out 4o image generation... It doesn't seem to support this use-case at all? I gave it an image of myself and asked to turn me into a wizard, and it generate something that doesn't look like me in the slightest. A second attempt, I asked to add a wizard hat and it just used python to add a triangle in the middle of my image. I looked at the examples and saw they had a direct image modification where they say "Give this cat a detective hat and a monocle", so I tried that with my own image "Give this human a detective hat and a monocle" and it just gave me this error:

> I wasn't able to generate the modified image because the request didn't follow our content policy. However, I can try another approach—either by applying a filter to stylize the image or guiding you on how to edit it using software like Photoshop or GIMP. Let me know what you'd like to do!

Overall, a very disappointing experience. As another point of comparison, Grok also added image generation capabilities and while the ability to edit existing images is a bit limited and janky, it still manages to overlay the requested transformation on top of the existing image.

Even when I told it to transform it into a text description, then draw that text description, my earlier attempt at a cat picture meant that the description was too close to a banned image...

I can't help but feel like openAI and grok are on unhelpful polar opposites when it comes to moderation.

In the coming days, people will Anime all sorts of images, for example historical images: https://x.com/keysmashbandit/status/1904764224636592188

Iterations are the missing link. With ChatGPT, you can iteratively improve text (e.g., "make it shorter," "mention xyz"). However, for pictures (and video), this functionality is not yet available. If you could prompt iteratively (e.g., "generate a red car in the sunset," "make it a muscle car," "place it on a hill," "show it from the side so the sun shines through the windshield"), the tools would become exponentially more useful.

I‘m looking forward to try this out and see if I was right. Unfortunately it’s not yet available for me.

nah. i pass and stick with midjourney.

EDIT: Seems not, "The smallest image size I can generate is 1024x1024. Would you like me to proceed with that, or would you like a different approach?"

If the subject matter is paywalled, I feel that the post should include some explanation of what is newsworthy behind the link.

How easy is this to remove? Is it just like exif data that can be easily stripped out, or is it baked in more permanently somehow

I couldn't find anything on the pricing page.

https://imgur.com/a/aS8e0UY

Sorry, but how are these useful? None of the examples demonstrate any use beyond being cool to look at.

The article vaguely mentions 'providing inspiration' as possible definition of 'useful'. I suppose.

Otherwise impressive.

Am I the only one immediately looking past the amazing text generation, the excellent direction following, the wonderful reflection, and screaming inside my head, "That's not how reflection works!"

I know it's super nitpicky when it's so obviously a leap forward on multiple other metrics, but still, that reflection just ain't right.

I think it is too biased to use heuristics discovered in the first response to apply the same level of compute to subsequent requests.

It makes me kind of want to rewrite an interface that builds appropriate context and starts new chats for every request issued..

--

Comparison with Leonardo.Ai.

ChatGPT: https://chatgpt.com/share/67e2fb21-a06c-8008-b297-07681dddee...

ChatGPT again (direct one shot): https://chatgpt.com/share/67e2fc44-ecc8-8008-a40f-e1368d306e...

ChatGPT again (using word "photorealistic instead of "photo"): https://chatgpt.com/share/67e2fce4-369c-8008-b69e-c2cbe0dd61...

Leonardo.Ai Phoenix 1.0 model: https://cdn.leonardo.ai/users/1f263899-3b36-4336-b2a5-d8bc25...

And I hope that people who worked on this know this. They are pure evil.

so much fun.

...Once the wait time is up, I can generate the corrected version with exactly eight characters: five mice, one elephant, one polar bear, and one giraffe in a green turtleneck. Let me know if you'd like me to try again later!

Was it public information when Google was going to launch their new models? Interesting timing.

ofc 4.5 is best, but its slow and I am afraid I'm going to hit limits.