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Anonymous 08/21/23(Mon)21:18:49 No.95513368▶>>95513511 >>95516463 >>95516875 >>95517242 >>95521840 >>95522448 >>95522633 >>95522690 >>95523089 >>95527863 >>95531321
Researchers discover that Stable Diffusion v1 uses internal representations of 3D geometry when generating an image. This ability emerged during the training phase of the AI, and was not programmed by people. Paper: "Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model".

https://arxiv.org/abs/2306.05720

Artificial intelligence systems like Stable Diffusion can create realistic-looking images from text prompts. But how do they actually do this? Researchers wondered if these systems build an internal understanding of 3D scenes, even though they only see 2D images during training.

To test this, they used a technique called "probing" to see if Stable Diffusion's [v1] internal workings contained any information about depth and foreground/background distinctions. Amazingly, they found simple representations of 3D geometry buried [located] in the code [AI's neurons]!

These depth and foreground/background representations formed very early when generating an image, before the image was [would be] clear to humans. By tweaking the internal [3D] geometry representations, the researchers could manipulate the final image's depth and positioning.

This means Stable Diffusion [v1] isn't just matching [superficial] patterns of pixels to text [that were learned during training]. Without ever seeing real 3D data, it learned its own rough model of the 3D world. The AI seems to "imagine" a simple 3D scene to help generate realistic 2D images.
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