This repository contains a PyTorch implementation of diffusion models, developed from first principles by two contributors. Key features include a cosine noise schedule, class-guided diffusion (without an external classifier), and training examples on CIFAR-10 and MNIST.
- Diffusion Model: Implemented from scratch in PyTorch.
- Cosine Schedule: Noise schedule following a cosine pattern for improved image quality.
- Class-Guided Diffusion: Directly conditioned on class labels, without requiring an external classifier.
- Training Datasets: Trained on CIFAR-10 and MNIST.
A sample GIF demonstrating the model generating a new image is provided below.
