💡 This paper investigates the properties of a latent manifold that are favorable for diffusion models, which are a type of generative model. The authors argue that existing methods for defining the latent space, known as tokenizers, are primarily designed to improve reconstruction fidelity or inherit pre-trained representations, but do not necessarily produce a latent space that is well-suited for generative modeling. To address this issue, the authors study the properties of a diffusion-friendly latent manifold and identify three key properties: coherent spatial structure, local manifold continuity, and global manifold semantics. They find that these properties are more closely related to downstream generation quality than reconstruction fidelity.

To explicitly shape the latent manifold with these desirable properties, the authors propose a new method called the Prior-Aligned AutoEncoder, or PAE. The PAE uses refined priors derived from variational autoencoders and perturbation-based regularization to turn the desired properties of the latent manifold into explicit training objectives. This approach allows the PAE to directly optimize the latent space structure for improved generative modeling.

The authors evaluate the PAE on the ImageNet 256x256 dataset and find that it improves both training efficiency and generation quality compared to existing tokenizers. Specifically, the PAE achieves comparable performance to the state-of-the-art method, RAE, but with up to 13 times faster convergence under the same training setup. Additionally, the PAE achieves a new state-of-the-art result, with a generative fidelity score of 1.03. These results highlight the importance of organizing the latent manifold for latent diffusion models and demonstrate the effectiveness of the PAE in producing high-quality generative models.