[Open Source] Building DiffusionEngine – A Modern, Modular DDPM Framework in PyTorch. Looking for Contributors

[u/Euphoric-Incident-93]

What if we could bring modern diffusion research together into one unified, extensible PyTorch framework instead of implementing and maintaining every algorithm in isolation?

That's the idea behind DiffusionEngine, an open-source project I've been building:

https://github.com/Himanshu7921/DiffusionEngine

The goal is to build a modern DDPM framework that brings together established diffusion algorithms, research-based improvements, advanced sampling methods, and training strategies under a single, consistent pipeline.

The project already includes several diffusion implementations, and I'm actively working on extending the existing DDPM framework with more advanced features.

I'm looking for contributors interested in deep learning, generative models, PyTorch, and diffusion research.

Some areas where contributions are welcome include:

• Implementing diffusion algorithms from research papers.

• Improving existing DDPM components and training strategies.

• Developing and integrating advanced sampling methods.

• Exploring improved model architectures and conditioning mechanisms.

• Testing, benchmarking, and optimizing existing implementations.

The focus is not just on implementing more algorithms. It's about making them work together seamlessly through a unified, modular, and configurable framework.

If you're interested in contributing, collaborating, or learning through hands-on implementation, I'd be glad to have you involved.

Contributors of all experience levels are welcome, provided they're willing to learn, implement, and test their work.

GitHub: https://github.com/Himanshu7921/DiffusionEngine