Compresses images into a small latent space where diffusion actually happens, then decodes the result back to pixels.
Latent diffusion models never diffuse pixels directly - a variational autoencoder first compresses the image ~8× per side into a compact latent tensor, the diffusion process denoises in that space, and the VAE decoder renders the final latent back into pixels. This is the trick that made high-resolution diffusion affordable, and it is why generation graphs end with a VAE decode stage.
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