Modular Diffusion – A modular Python library for diffusion models
Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process…
In plain words
Modular Diffusion is a Python library for designing and training diffusion models with a flexible, modular architecture. It allows researchers and developers to quickly prototype diffusion models by combining different components like noise schedules, denoising networks, and loss functions. The library includes pre-built modules and supports custom implementations, enabling users to experiment with various configurations in just a few lines of code.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process (noise schedule, noise type, denoising network, loss function, guidance, etc.) and allow the user to mix and match different modules to achieve different results. The library ships with a bunch of prebuilt modules and the plan is to add many more. I also made it super easy to implement your own modules, you just need to extend from one of the base classes available. Contrary to HuggingFace Diffusers, this library is focused on designing and training your own Diffusion Models rather than finetuning pretrained ones (although this is possible). I would really appreciate your feedback.
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