DDPM-NumPy-from-Scratch

This repository contains a Denoising Diffusion Probabilistic Model (DDPM) implemented purely using NumPy for demonstration and educational purposes.

The goal of this project is to showcase a deep, fundamental understanding of Diffusion Models by building the core training and inference mechanics from scratch, without relying on high-level frameworks like PyTorch or TensorFlow.

🧠 Core Algorithmic Mechanics

This implementation includes a custom, low-level implementation of all critical components:

• Forward and Reverse Diffusion: Full implementation of the noise scheduling process.
• Time Embedding: Positional encoding of the timestep variable, essential for the Diffusion Model's MLP (Noise Predictor).
• Custom Backpropagation: Manual gradient calculation for the MLP, demonstrating a solid grasp of deep learning fundamentals.
• AdamW Optimizer: Implementation of the AdamW optimization algorithm from scratch.
• Noise Scheduling: Utilization of the standard Cosine Scheduler for stable training.

🛠️ Project Structure and Verification

The clean structure ensures that the core logic is separate from the testing utilities:

• src/ddpm_numpy_training.py: The complete core DDPM training and inference logic.
• tests/test_ddpm_core.py: Comprehensive Unit Tests to verify the shape and correctness of all core functions (scheduler, time embedding, AdamW update, etc.).

✅ Getting Started (Requirements)

Given the nature of the project, only the NumPy library is required:

pip install numpy