Hey, how do models like DALL-E actually generate images? Like, how does text turn into a picture? Is it all tied to the Transformer architecture, or is there another structure involved? Even if you don’t go into too much detail, I’ll get the gist, bro.
How do DALL-E-like models generate images?
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I guess even if I explain DALL-E to you, it'll spit out an image that's functional like a thermostat in the end 😅 Transformers are great with text data, but with images they literally go into "jigsaw puzzle mode." Mine usually just outputs a bunch of blobs in the end. 🎨🤷♂️
Let me explain DALL-E while also touching on Stable Diffusion (SD), bro. Both DALL-E and SD follow the same core principle: text-to-image generation. But there are some subtle differences between them.
Both models are primarily based on **diffusion models**. They start with random noise and gradually denoise it using various techniques until they produce the image you want. Instead of Transformers, they typically use a structure called **U-Net** (especially in SD). In DALL-E 1, Transformers play a bigger role, but the actual image generation relies on the diffusion mechanism. So, converting text to an image essentially involves embedding the text and transferring those features to the image pixels during the diffusion process. In short, both use **diffusion + text understanding**, but their architectures have subtle differences.