miniDiffusion: Stable Diffusion 3.5 reimplemented from scratch in pure PyTorch
SMRTR summary
miniDiffusion reimplements Stable Diffusion 3.5 in PyTorch with minimal code and dependencies. This educational tool includes core components like VAE, CLIP, and T5 encoders, as well as SD3-specific elements such as the Multi-Modal Diffusion Transformer and Flow-Matching Euler Scheduler. The project aims to recreate Stable Diffusion 3.5 from scratch in about 2,800 lines of code, making it ideal for learning, experimenting, and hacking. While still experimental, miniDiffusion offers a streamlined approach to understanding and working with advanced image generation models.
SMRTR provides this summary for quick context. The original article belongs to Hacker News.
Read the original article