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LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models

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arxiv 2405.14477 v2 pith:5ECMBKMC submitted 2024-05-23 cs.LG cs.CV

Seyedmorteza Sadat , Jakob Buhmann , Derek Bradley , Otmar Hilliges , Romann M. Weber This is my paper · ORCID
classification cs.LGcs.CV
keywords litevaeldmsqualitytrainingvaesautoencoderautoencodersdesign
verification ladder T0 review T1 audit T2 compute T3 formal

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Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains underexplored. In this paper, we introduce LiteVAE, a new autoencoder design for LDMs, which leverages the 2D discrete wavelet transform to enhance scalability and computational efficiency over standard variational autoencoders (VAEs) with no sacrifice in output quality. We investigate the training methodologies and the decoder architecture of LiteVAE and propose several enhancements that improve the training dynamics and reconstruction quality. Our base LiteVAE model matches the quality of the established VAEs in current LDMs with a six-fold reduction in encoder parameters, leading to faster training and lower GPU memory requirements, while our larger model outperforms VAEs of comparable complexity across all evaluated metrics (rFID, LPIPS, PSNR, and SSIM).

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Cited by 5 Pith papers

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