Denoising Diffusion Probabilistic Models (DDPM)

Overview

Denoising Diffusion Probabilistic Models (DDPM) are a class of generative models that learn to generate data by reversing a diffusion process. They have gained popularity for their ability to produce high-quality samples in various domains, including images, audio, and text.

Key Concepts

1. Diffusion Process: This is a process where data is gradually corrupted by noise over a series of steps. The model learns to reverse this process, effectively denoising the data.
2. Training: The model is trained on a dataset by simulating the diffusion process and learning to predict the original data from the noisy version at each step.
3. Sampling: Once trained, the model can generate new samples by starting from random noise and applying the learned reverse diffusion process.

Processes

1. Training Process

• Noise Addition: Gradually add noise to the data over a fixed number of steps, creating a series of noisy images.
• Model Training: Train the model to predict the original data from the noisy versions using a loss function that measures the difference between the predicted and actual data.

2. Sampling Process

• Initialization: Start with a sample of random noise.
• Reverse Diffusion: Iteratively apply the learned model to denoise the sample, gradually transforming it into a coherent output.
• Output Generation: The final output is a generated sample that resembles the training data.

Installation

To install the required dependencies, run:

pip install -r requirements.txt

Usage

To run the DDPM model, execute:

python ddpm.py

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Original Paper - Denoising Diffusion Probabilistic Models

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