Denoising Diffusion Models on Model-Based Latent Space
Carmelo Scribano,
Danilo Pezzi,
Giorgia Franchini
et al.
Abstract:With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away imperceptible image details and introducing substantial spatial compression, renders the learning of the generative process more manageable while significantly reducing computational and memory demands. In this work, we propose to replace autoencoder coding wi… Show more
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