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October 2, 2025Open Access

Latent Diffusion Models with Masked AutoEncoders

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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.

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Analysis reveals limitations of current autoencoders in latent diffusion models, suggesting improved designs with masked architectures.

Lee et al. (2025) studied this question.

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