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PaperclipPerfector  4mo ago (text post)   38813 thread views

Culture War Roundup for the week of July 15, 2024

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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.

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