# 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:

```bash
pip install numpy