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October 2, 2025Open Access

Latent Diffusion Models with Masked AutoEncoders

Key Points

Existing autoencoders struggle to ensure all key properties, limiting their effectiveness in latent diffusion models.
The study identifies latent smoothness, perceptual compression quality, and reconstruction quality as crucial for effective autoencoders.
Variational Masked AutoEncoders are proposed to better maintain hierarchical features, enhancing performance in latent diffusion models.
By integrating VMAEs into the LDM framework, this approach aims at improving the overall quality of image generation tasks.

Abstract

In spite of the remarkable potential of Latent Diffusion Models (LDMs) in image generation, the desired properties and optimal design of the autoencoders have been underexplored. In this work, we analyze the role of autoencoders in LDMs and identify three key properties: latent smoothness, perceptual compression quality, and reconstruction quality. We demonstrate that existing autoencoders fail to simultaneously satisfy all three properties, and propose Variational Masked AutoEncoders (VMAEs), taking advantage of the hierarchical features maintained by Masked AutoEncoders. We integrate VMAEs into the LDM framework, introducing Latent Diffusion Models with Masked AutoEncoders (LDMAEs).

Latent Diffusion Models with Masked AutoEncoders

Key Points

Abstract

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