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Autoencoders for Image-to-Latent Compression for Diffusion Models

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Autoencoders for Image-to-Latent Compression for Diffusion Models

Now we know how the text portion of input is processed before being passed to U-Net during training and inference. Let's now explore how the image portion of input is processed.

Before passing the input image to U-Net, we first pass it to a pre-trained variational autoencoder to compress the image into a latent.

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