Diffusion models were introduced in 2015 as a method to train a model that can sample from a highly complex probability distribution. They used techniques from non-equilibrium thermodynamics, …

Following the Latent Diffusion Model, we use a pre-trained Variational Autoencoder (VAE) to compress the images into low-dimensional latent spaces and train a diffusion model to learn the data …

2022年12月19日 · We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a …

The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. Transformers is a library produced by Hugging Face that supplies …

2021年7月11日 · U-net is a common choice of model architecture in diffusion modeling for high-resolution image generation. A cascaded pipeline of multiple diffusion models at increasing resolutions.

2022年6月7日 · What is a diffusion model? A (denoising) diffusion model isn't that complex if you compare it to other generative models such as Normalizing Flows, GANs or VAEs: they all convert …

Diffusion models of protein structure; trigonometry and attention are all you need! - microsoft/foldingdiff

This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper exploring diffusion models with transformers (DiTs). You can find more visualizations on our …

Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the …

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch. - huggingface/diffusers