Diffusion Model for Image Generation

A diffusion model implementation using U-Net architecture for generating high-quality 128×128 images.

Overview

This project implements a diffusion probabilistic model that learns to generate images by iteratively denoising random noise. The model uses the U-Net architecture as its backbone, which has proven highly effective for image-to-image translation tasks and diffusion-based generation.

Architecture

The model is built on the U-Net architecture, which features:

• Encoder path: Progressively downsamples the input through convolutional blocks, capturing hierarchical features
• Bottleneck: Processes the most compressed representation of the image
• Decoder path: Upsamples the features back to the original resolution
• Skip connections: Preserve fine-grained spatial information by connecting encoder and decoder layers directly

Image credit: eviltux.com

How Diffusion Models Work

Diffusion models operate through two main processes:

1. Forward diffusion: Gradually adds noise to training images over multiple timesteps until they become pure random noise
2. Reverse diffusion: The U-Net learns to reverse this process, predicting and removing noise step-by-step to generate clean images from random noise

Model Specifications

• Output resolution: 128×128 pixels
• Architecture: U-Net with time-step conditioning
• Task: Unconditional/conditional image generation
• Training: Dataset-agnostic (configurable for any image dataset)

Key Features

• Flexible dataset support: Train on any image dataset by simply organizing images in the appropriate directory structure
• Configurable generation: Control the number of diffusion steps for quality vs. speed trade-offs
• Scalable architecture: Can be adapted for different image resolutions and conditioning methods

Training Process

The model learns to:

1. Predict the noise added at each diffusion timestep
2. Gradually denoise random noise into coherent images
3. Capture the statistical distribution of the training dataset

Use Cases

This diffusion model can be applied to:

• Generative art: Create novel images in the style of your training data
• Data augmentation: Generate synthetic training samples for downstream tasks
• Image synthesis research: Experiment with diffusion-based generation techniques
• Custom dataset generation: Train on specific domains (faces, landscapes, objects, etc.)