Apr 24, 2023 · We illustrate how one can extract a latent space from a pre-existing diffusion model by optimizing an encoder to maximize the marginal data log-likelihood. Furthermore, we demonstrate that a decoder can be analytically derived post encoder-training, employing the Bayes rule for scores.

Apr 14, 2025 · In this paper we tackle a fundamental question: "Can we train latent diffusion models together with the variational auto-encoder (VAE) tokenizer in an end-to-end manner?" Traditional deep-learning wisdom dictates that end-to-end training is often preferable when possible. However, for latent diffusion transformers, it is observed that end-to-end training both VAE and diffusion-model using ...

We utilize a Variational Autoencoder (VAE) to encode these heterogeneous features into continuous representations in a latent space. The VAE reconstructs the original input and generates new data points. We then use a diffusion model to generate new latent representations from the learned continuous representations.

Sep 2, 2024 · We will start by building a Variational Auto Encoder. We will build the Encoder and decoder. A Variational Autoencoder (VAE) is a type of neural network that can learn to compress data (like...

Implementation of Variational Diffusion Auto-encoder: Latent Space Extraction from Pre-trained Diffusion Models (Batzolis++23) in jax and equinox. The idea here is to remedy the assumption a traditional variational autoencoder (VAE) on the reconstruction likelihood $p(\boldsymbol{x}|\boldsymbol{z})$ assumption by building the likelihood of the ...

This work shows that it is possible to create a VAE-like deep latent variable model without making the Gaussian assumption on p ( x | z ) or even training a decoder network. Variational auto-encoders (VAEs) are one of the most popular approaches to deep generative modeling.

4 days ago · Base model: hybrid diffusion variational autoencoder. ... The encoder is DimeNet 63, ... M. Auto-encoding variational Bayes. Preprint at https: ...

Nov 20, 2024 · Variational Autoencoders (VAEs) employ an encoder that transforms input data into a probability distribution over latent variables, diverging from traditional autoencoders. This distribution...

Jun 23, 2024 · We tackle this problem by combining the ideas of the variational auto-encoder (VAE) and the denoising diffusion probabilistic model (DDPM).

To remove topological obstructions, we introduce Diffusion Variational Autoencoders ( VAE) with arbitrary (closed) manifolds as a latent space. A Diffusion Variational Autoencoder uses transition kernels of Brownian motion on the manifold.