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Comment by andrelaszlo | Hacker News Reader

Here's a custom prompt that I enjoyed:

"Think hard about every single detail of the image, conceptualize it including the style, colors, and lighting.

Final step, condensing this into a single paragraph:

Very carefully, condense your thoughts using the most prominent features and extremely precise language into a single paragraph."

https://dalle.party/?party=1lSMniUP

https://dalle.party/?party=cEUyjzch

https://dalle.party/?party=14fnkTv-

https://dalle.party/?party=wstiY-Iw

Praise the Basilisk, I finally got rate-limited and can go to bed!

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Mine got surral real fast, though the sixth one is kinda cool https://dalle.party/?party=DNgriW_E

These are fantastic

The thing that is truly mindboggling to me is that THE SHADOWS IN THE IMAGES ARE CORRECT. How is that possible??? Does DALL-E actually have a shadow-tracing component?

Yes! It can also get reflections and refractions mostly correct.

Stable diffusion does decent reflections too

Research into the internals of the networks have shown that they figure out the correct 2.5D representation of the scene before the RGB textures (internally), so yes it seems they have an internal representation of the scene and therefore can do enough inference from that to make shadows and light seem natural.

I guess it's not that far-fetched as your brain has to do the same to figure out if a scene (or an AI-generated one for that matter) has some weird issue that should pop out. So in a sense your brain does this too.

Interesting! Do you have a link to that research?

Certainly: https://arxiv.org/abs/2306.05720

It's a very interesting paper.

"Even when trained purely on images without explicit depth information, they typically output coherent pictures of 3D scenes. In this work, we investigate a basic interpretability question: does an LDM create and use an internal representation of simple scene geometry? Using linear probes, we find evidence that the internal activations of the LDM encode linear representations of both 3D depth data and a salient-object / background distinction. These representations appear surprisingly early in the denoising process−well before a human can easily make sense of the noisy images."

What does 2.5D mean?

It means you should be worried about the guy she told you not to worry about

You usually say 2.5D when it's a 3D but only from a single vantage point with no info of the back-facing side of objects. Like the representation you get from a depth-sensor on a mobile phone, or when trying to extract depth from a single photo.

I randomly checked a few links here and shadows were correct in 2 images out of a dozen... and any people tend to be horrifying in many

> https://dalle.party/?party=14fnkTv-

Interesting that for one and only one iteration, the anthropomorphized cardboard boxes it draws are almost all Danbo: https://duckduckgo.com/?q=danbo+character&ia=images&iax=imag...

It was surprising to see a recognizable character in the middle of a bunch of more fantastical images.

Short focal length was a neat idea, it let it left lots of room for the subsequent iterations to fill in the background.

> https://dalle.party/?party=1lSMniUP

It's very interesting to observe how the relationship between the wolf and Redhood evolved from dark and menacing to serene and friendly.

The fractal one is awesome!

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