CRN rounds up Nvidia’s biggest news stories of 2025 so far, ranging from its $4 trillion market cap milestone and ongoing ...

OmniHuman-1 can create natural-looking human videos using only a single reference image and synchronized audio input.

OmniGen relies on two core components: a Variational Autoencoder—the good old VAE that all AI artists are so familiar with—that deconstructs images into their fundamental building blocks, and a ...

The AI art scene is getting hotter. Sana, a new AI model introduced by Nvidia, runs high-quality 4K image generation on consumer-grade hardware, thanks to a clever mix of techniques that differ a bit ...

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 ...

The proposed models are trained on an Nvidia A100 GPU using the ImageNet dataset at various resolutions. Moreover, techniques like classifier-free guidance are applied during training, and a ...

Model architecture Stable Diffusion uses a variational autoencoder (VAE) to generate detailed images from a caption with only a few words. Unlike prior autoencoder-based diffusion models, Stable ...