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

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

Stable Diffusion implemented from scratch in PyTorch

[ACM MM 2023] - LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On

This is the official repository for the paper "Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing". ICCV 2023

[CVPR 2023] Collaborative Diffusion

A novel inpainting framework that can remove objects from images based on the instructions given as text prompts.

This repo implements a Stable Diffusion model in PyTorch with all the essential components.

Playing around with stable diffusion. Generated images are reproducible because I save the metadata and latent information. You can generate and then later interpolate between the images of your choice.

Author's Implementation for E-LatentLPIPS

[Early Accepted at MICCAI 2023] Pytorch Code of "InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model"

Easy setup for generating beautiful images with Stable Cascade.

Repository to train Latent Diffusion Models on Chest X-ray data (MIMIC-CXR) using MONAI Generative Models

Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.

Official PyTorch implementation of "Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging". LDAE is a novel unsupervised framework for 3D medical imaging that combines a latent diffusion model with semantic controls.

Research repository for diffusion based structure based drug design

A high-performance, memory-efficient inference server for diffusion models, compatible with the OpenAI client

[official] PyTorch implementation of Latent Diffusion Model for Conditional Reservoir Facies Generation

This repo implements Video generation model using Latent Diffusion Transformers(Latte) in PyTorch and provides training and inference code on Moving mnist dataset and UCF101 dataset

Unlock the potential of latent diffusion models with MNIST! 🚀 Dive into reconstructing and generating digits using cutting-edge techniques like Autoencoders with Channel Attention Blocks and DDPMs. Perfect for enthusiasts of computer vision, deep learning, and generative modeling! 🌌✨