Experts at MIT and Nvidia created a hybrid approach for AI image generation that takes far less computation resources while retaining high visual details.

The Vector Quantized Variational AutoEncoder (VQ-VAE) has shown great potential in image generation, especially the methods with hierarchical features. However, the lack of decoupling of structural ...

This repository contains a Jupyter notebook implementing a Vanilla Variational Autoencoder (VAE) for image generation. The VAE is a powerful generative model that learns to encode images into a latent ...

VARIATIONAL AUTOENCODER Auteurs : BOUTAUD DE LA COMBE Baptiste BOUSSOUF Noâm FAUCHEUX Jérôme PU Zhenyu Ce répertoire est constitué du support de présentation utilisé lors de la soutenance du ...

The utilization of deep VAEs for image generation has enabled remarkable progress in the field of image generation. However, hierarchical VAEs have yet to produce high-quality images on large, diverse ...