MurrellGroup/Flowfusion.jl
Unifying flow matching and diffusion on Riemannian manifolds and discrete spaces, for generative deep learning.
This is a Julia package designed for machine learning researchers and practitioners working with generative deep learning. It helps you build and train models that can generate new data points by learning how to transform one distribution into another. You provide starting and ending data distributions, and the tool helps create a model that can generate diverse, high-quality samples matching the target distribution, even across complex data types like continuous, discrete, or manifold data.
Use this if you are a machine learning researcher or deep learning practitioner who needs to generate synthetic data, fill in missing data, or explore complex data transformations across various data structures, including those on geometric manifolds.
Not ideal if you are looking for a pre-trained, out-of-the-box generative model for common tasks, or if you are not comfortable with deep learning model development in Julia.
Stars
38
Forks
5
Language
Julia
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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