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PaperclipPerfector  4mo ago (text post)   38814 thread views

Culture War Roundup for the week of July 15, 2024

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LLMs don’t generate pictures. I have no idea why people keep repeating the blatantly obviously incorrect claim that AI equals LLM.

Stable diffusion contains a text transformer. Language models alone don't generate pictures but they're a necessary part of the text-to-image pipeline.

Also some LLMs can use tools, so an LLM using an image generation tool is in a sense generating a picture. It's not like humans regularly create pictures without using any tools.

Yes, it has an input parser. If you’ve studied SD details, you’ll know that it’s very different from what people call LLMs and is only a small part of Stable Diffusuon (and not anything you could say that ”generates pictures”).

Yes, it has an input parser

Specifically OpenCLIP. As far as I can tell the text encoder is nearly a bog-standard GPT-style transformer. The transformer in question is used very differently than the GPT-style next token sampling loop, but architecturally the TextTransformer it's quite similar to e.g. gpt-2.

Still, my understanding is that the secret sauce of stable diffusion is that it embeds the image and the text into tensors of the same shape, and then tries to "denoise" the image in such a way that the embedding of the "denoised" image is closer to the embedding of the text.

The UNet is the bit that generates the pictures, but the text transformer is the bit which determines which picture is generated. Without using a text transformer, CLIP and thus stable diffusion would not work nearly as well for generating images from text. And I expect that further advancements which improve how well instructions are followed by image generation models will come mainly from figuring out how to use larger language transformers and a higher dimensional shared embedding space.

Tbh, I didn’t notice that Tomato had called those out in particular. The top level post talks about various applications which are definitely not LLMs.

LLMs specifically are horrible with arithmetic (as Tomato said). I don’t see why a math oriented AI couldn’t be made - it just wouldn’t be an LLM and quite possibly would have about as much in common with LLM as eg. image generators do (iow, very little beyond an input parsing stage).

I don’t know what it is about this site (other than people being infatuated with ridiculously long meandering posts) that makes users think LLMs are the modal example of AI when their actual productive uses are limited to a few text generating and parsing niches. Meanwhile eg. every photo 99% of people take has multiple layers of AI applied to it.

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