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This paper introduces diffusion autoencoders, which utilize diffusion probabilistic models (DPMs) to create a meaningful and decodable representation of images. The proposed method encodes images into a two-part latent code capturing high-level semantics and low-level stochastic variations, allowing for near-exact reconstruction and facilitating various downstream tasks. By leveraging a learnable encoder and a conditional DPM decoder, the approach enhances denoising efficiency and supports applications such

Author

tinhthanniemsu

Language

EN