This makes it highly memory-consuming for applying advanced deep learning methods, such as the transformer and diffusion model, to FODs represented by high order SPHARMs. In this work, we present an ...

This article introduces a novel prediction model, called the learning autoencoder diffusion model (LADM) of pedestrian group relationships for multimodal trajectory prediction, which takes into ...

Unlike prior autoencoder-based diffusion models, Stable Diffusion incorporates a U-Net backbone with cross-attention layers to reduce noise while learning the latent representation. This enables the ...

This project implements a latent diffusion model for generating highly realistic facial images. The model first projects input images to a latent space using an autoencoder and then trains a diffusion ...

This repository will soon contain the code for our paper "ClimSat - A Diffusion Autoencoder Model for Climate-conditional Satellite Image Editing" published in Science of Remote Sensing in 2025.

By parameterizing the denoising autoencoder as a low-rank model, it is shown that optimizing the training loss of diffusion models is equivalent to solving a subspace clustering problem. This ...

The variational autoencoder (VAE), which compresses source ... as well as the first to explore the new Stable Diffusion model for generating medical images. Admittedly, several limitations ...

The model encodes the semantic meaning of the prompt to guide the image generation process. Latent Space Representation: Stable Diffusion uses a Variational Autoencoder (VAE) to compress images ...