101–110 of 629 posts
Earlier quoted context omitted.
It's a nitpick about the repetitive phrasing for announcements : Our most yet|ever.
I hate modern marketing trends. This one isn't even my biggest gripe. If I could eliminate any word from the English language forever, it would be "effortlessly".
This works great for many purposes. 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
It just doesn't have that kind of image editing capability. Maybe people just assume it does because Google's similar model has it. But did OpenAI claim it could edit images?
To quote myself from a comment on sora: 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 windshie…
For example, https://news.ycombinator.com/item?id=43388114
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.
What's important about this new type of image generation that's happening with tokens rather than with diffusion, is that this is effectively reasoning in pixel space. 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 l…
Earlier quoted context omitted.
It's a nitpick about the repetitive phrasing for announcements : Our most yet|ever.
I hate modern marketing trends. This one isn't even my biggest gripe. If I could eliminate any word from the English language forever, it would be "effortlessly".
To quote myself from a comment on sora: 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 windshie…
Reading other comments in other threads on HN has left me with the impression that iterative improvement within a single chat is not a good idea. For example, https://news.ycombinator.com/item?id=43388114
Earlier quoted context omitted.
Structural editing and control nets are much more powerful than text prompting alone. The image generators used by creatives will not be text-first. "Dragon with brown leathery scales with an elephant texture and 10% reflectivity positioned three degrees under the mountain, which is approximately 250 meters taller than the next peak, ..." is not how you design. Creative work is not 100% dice rolling in a crude and in…
Prompt adherence and additional tricks such as ControlNet/ComfyUI pipelines are not mutually exclusive. Both are very important to get good image generation results.
Still seems to have problems with transparent backgrounds.
That's expected with any image generating models because they aren't trained with an alpha channel. It's more pragmatic to pipeline the results to a background removal model. EDIT: It appears GPT-4o is different as there is a video demo dedicated to transparancy.