nanoDiffusion

nanoDiffusion is the simplest PyTorch-based diffusion model implementation, engineered for rapid, accessible training on consumer hardware (e.g., Apple M chips).

This is nanoDiffusion: a streamlined, minimal implementation of a diffusion model in PyTorch. It provides a fast-track entry into generative AI, supporting both DDPM and DDIM samplers. The architecture is specifically optimized for accessibility, featuring native acceleration support for Apple M-series chips. This efficiency allows developers to train a decent model on the MNIST dataset in just 10 to 30 minutes on a standard MacBook. We cut the complexity, focusing on core functionality and speed: a powerful tool for quick prototyping and educational use in the diffusion space.

https://github.com/changgyhub/nanoDiffusion