Latent Space
Latent space is the compressed mathematical representation where a diffusion model does its work before decoding back into a final image.
Modern diffusion models don't operate directly on pixels — that would be computationally enormous. Instead, they work in a compressed "latent space" where an entire image is represented as a relatively small tensor of numbers. A separate neural network (the decoder) translates these latents back into pixel images.
This architecture is why modern image models can run on consumer GPUs: the heavy lifting happens in a space roughly 1/64th the size of the final image. It's also what enables techniques like latent interpolation (smoothly blending between two images by blending their latent representations).
You don't need to understand latent space to use an AI art tool, but it helps explain why certain techniques — inpainting, outpainting, style transfer — work the way they do.
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