This repository is a comprehensive resource for mastering generative AI, featuring in-depth notes and exciting projects. The goal is to stay updated with the latest advancements in generative AI, and explore applications in image & video generation, creative content creation. Explore the limitless possibilities of generative AI today!

A simple, easy-to-understand library for diffusion models using Flax and Jax. Includes detailed notebooks on DDPM, DDIM, and EDM with simplified mathematical explanations. Made as part of my journey for learning and experimenting with state-of-the-art generative AI.

The torchmanager implementation for diffusion models.

PyTorch denoising diffusion demo

[MICCAI2024 Oral] LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion

🎨"Denoising Diffusion Probabilistic Models" paper implementation. a stable diffusion engine: using pytorch as a backend and fastAPI as frontend using javascript, and slo providing gradio interface

A flexible and modular learning platform for medical images

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

A PyTorch implementation of DDPM

IDDM (Industrial, landscape, animate...), support DDPM, DDIM, PLMS, webui and multi-GPU distributed training. Pytorch实现，生成模型，扩散模型，分布式训练

Official PyTorch implementation for the paper Star-Shaped Denoising Diffusion Probabilistic Models

a simple implementation of ddpm

[MICCAI 2024] Easy diffusion models (optionally with segmentation guidance) for medical images and beyond.

Ensemble model for face morphing attack detection built using diffusion model and siamese model

code for the paper "GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?"

An implementation of 'simple diffusion: End-to-end diffusion for high resolution images' as published by Hoogeboom et al.

An implement of DDPM in Pytorch

Unofficial PyTorch Implementation of Denoising Diffusion Probabilistic Models (DDPM)

code for the paper "LayerDAG: A Layerwise Autoregressive Diffusion Model of Directed Acyclic Graphs"