smsharma/minified-generative-models
Bare-bones implementations of some generative models in Jax: diffusion, normalizing flows, consistency models, flow matching, (beta)-VAEs, etc
This project provides straightforward, minimal examples of generative models like VAEs, diffusion models, and normalizing flows. It takes common data structures and demonstrates how these models can generate new, similar data or learn underlying data distributions. It's designed for machine learning researchers, students, or practitioners who want to understand the core mechanics of various generative models without excessive complexity.
141 stars. No commits in the last 6 months.
Use this if you are studying or experimenting with generative artificial intelligence models and need clear, unembellished reference implementations to grasp their fundamental principles.
Not ideal if you need a production-ready library for deploying large-scale generative AI applications or require extensive features and optimizations beyond basic model implementations.
Stars
141
Forks
7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 20, 2023
Commits (30d)
0
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