Masked Autoencoders Are Effective Tokenizers for Diffusion Models

Recent advances in latent diffusion models have demonstrated their effectiveness for high-resolution image synthesis. However, the properties of the latent space from tokenizer for better learning and generation of diffusion models remain under-explored. Theoretically and empirically, we find that improved generation quality is closely tied to the latent distributions with better structure, such as the ones with fewer Gaussian Mixture modes and more discriminative features. Motivated by these insights, we propose MAETok, an autoencoder (AE) leveraging mask modeling to learn semantically rich latent space while maintaining reconstruction fidelity. Extensive experiments validate our analysis, demonstrating that the variational form of autoencoders is not necessary, and a discriminative latent space from AE alone enables state-of-the-art performance on ImageNet generation using only 128 tokens. MAETok achieves significant practical improvements, enabling a gFID of 1. 69 with 76$\times$ faster training and 31$\times$ higher inference throughput for 512$\times$512 generation. Our findings show that the structure of the latent space, rather than variational constraints, is crucial for effective diffusion models. Code and trained models will be released.

Authors

• Hao Chen 0102
• Yujin Han
• Fangyi Chen
• Xiang Li 0106
• Yidong Wang 0003
• Jindong Wang 0001
• Ze Wang 0008
• Zicheng Liu 0001
• Difan Zou
• Bhiksha Raj

Keywords

• Diffusion Models
• Autoencoders
• Masked Autoencoders