With better response to details and text, DALL-E 3 hopes to make prompt engineering obsolete.
On Wednesday, OpenAI announced DALL-E 3, the latest version of its AI image synthesis model that features full integration with ChatGPT. DALL-E 3 renders images by closely following complex descriptions and handling in-image text generation (such as labels and signs), which challenged earlier models. Currently in research preview, it will be available to ChatGPT Plus and Enterprise customers in early October.
Like its predecessor, DALLE-3 is a text-to-image generator that creates novel images based on written descriptions called prompts. Although OpenAI released no technical details about DALL-E 3, the AI model at the heart of previous versions of DALL-E was trained on millions of images created by human artists and photographers, some of them licensed from stock websites like Shutterstock. It's likely DALL-E 3 follows this same formula, but with new training techniques and more computational training time.
Judging by the samples provided by OpenAI on its promotional blog, DALL-E 3 appears to be a radically more capable image synthesis model than anything else available in terms of following prompts. While OpenAI's examples have been cherry-picked for their effectiveness, they appear to follow the prompt instructions faithfully and convincingly render objects with minimal deformations. Compared to DALL-E 2, OpenAI says that DALL-E 3 refines small details like hands more effectively, creating engaging images by default with "no hacks or prompt engineering required."
That avocado image is insane. I've yet to see any image prompt AI get text and composition anywhere near this level. Mindblowing to know there were zero edits. I really want to try this now.
One of the things I've noticed from testing these generators is that they look very good when someone is showing off cherry-picked photos or you are going in with little to no expectation of what you'll get. The more you have a specific image in mind or the more you want certain details the worse it gets. And you quickly realize that when you generate things from similar prompts over and over the model gives you the same results but slightly adjusted.
Obviously art generators look bad for artists right now, but I think once the new toy factor wears off people will realize they aren't as good as they seem. There's a lot of smoke and mirrors involved and once you've seen a good amount of AI photos it gets easier and easier to pick them out of a lineup. They're closer to advanced stock photo generators, in terms of what you can actually get from them. Companies' race to the bottom means that this is going to have effects on artist jobs, but I think the next "revolution" in art is going to be having human art as a selling point the same way stuff like fully orchestrated music or hand-crafted things are.
I wish more people realised this. It's much harder to create very specific images with the current image generation tools than most people seem to think, which is creating an inaccurate view of the technology in the public eye.
The generator will create something inspired by the prompt it is given, but it can be very hard to make it match the output the prompt writer imagines when writing the prompt. There are various tools that can refine and narrow the generator's output, to try and control things like posing, composition, style etc and to redraw details. But even then it's often pot luck as to the output. The generated images aren't necessarily bad, just not what was wanted.
I think the comparison to stock photo images is apt, current image generators are great for creating themed but somewhat generic images. The tools are going to continue to advance, and they are useful in for some applications already. But they are still a long way off from truly replacing human artistry.
Edit: I feel like the nuance of this joke may have been lost on some. Whether or not I read the article is irrelevant, since this was not a genuine question, rather a play on words of the double meaning of “china” as in “A woman from (the country) China” and “A woman (emerging) from china (porcelain)”.
While there is some truth in this, humans and AI do not make the same type of mistakes with hands.
Humans will rebuild the topological structure of the hand: 5 fingers protruding from a base, and get the proportions wrong..while the topology is credible.
AI will rebuild the image of a hand from the 2d appearance of a hand: a variable number of flesh colored, parallel stripes, and improvise from that.
While both can get it wrong, the errors are not similar.
On Wednesday, OpenAI announced DALL-E 3, the latest version of its AI image synthesis model that features full integration with ChatGPT.
DALL-E 3 renders images by closely following complex descriptions and handling in-image text generation (such as labels and signs), which challenged earlier models.
Judging by the samples provided by OpenAI on its promotional blog, DALL-E 3 appears to be a radically more capable image synthesis model than anything else available in terms of following prompts.
While OpenAI's examples have been cherry-picked for their effectiveness, they appear to follow the prompt instructions faithfully and convincingly render objects with minimal deformations.
DALL-E 3 also appears to handle text within images in a way that its predecessor couldn't (some competing models like Stable Diffusion XL and DeepFloyd are getting better at it).
Microsoft's Bing Chat AI assistant, also built on technology from OpenAI, has been able to generate images in conversation since March.
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