[P] Micro Diffusion — Discrete text diffusion in ~150 lines of pure Python

2026-02-28 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Micro Diffusion is a minimal implementation of a discrete text diffusion algorithm, inspired by Karpathy's MicroGPT, designed to demonstrate the core mechanics without excessive complexity. Unlike autoregressive models that generate text sequentially, this approach generates all tokens simultaneously by iteratively unmasking from noise. The project includes three distinct implementations: `train_minimal.py` (143 lines, pure NumPy), `train_pure.py` (292 lines, pure NumPy with comments and visualization), and `train.py` (413 lines, PyTorch with a bidirectional Transformer denoiser). All versions share the same diffusion loop, with only the denoiser component being pluggable and varying. The system trains on 32,000 SSA names and operates efficiently on a CPU within minutes, requiring no GPU.

Key takeaway

For AI Engineers or Machine Learning Engineers seeking to understand text diffusion, Micro Diffusion offers a highly simplified, CPU-friendly implementation. You should explore the `train_minimal.py` and `train_pure.py` scripts to grasp the fundamental algorithm without the overhead of complex frameworks, then examine `train.py` to see a PyTorch Transformer denoiser in action. This project provides a clear pathway to experiment with discrete text diffusion models efficiently.

Key insights

Discrete text diffusion generates all tokens at once by iteratively unmasking from noise.

Principles

• Diffusion denoisers are pluggable components.
• Minimal implementations clarify core algorithms.

Method

The method involves a shared diffusion loop across implementations, with denoisers varying from pure NumPy to a PyTorch bidirectional Transformer, iteratively unmasking tokens from noise.

In practice

• Implement text diffusion with pure NumPy.
• Train on CPU for rapid prototyping.

Topics

• Text Diffusion
• Discrete Diffusion Models
• Minimal AI Implementations
• Transformer Denoiser
• NumPy

Code references

• Siwoo4985/Micro-Diffusion

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.