U-Net Denoising Diffusion Probabilistic Model (DDPM)

Denoising Diffusion Probabilistic Model (DDPM) using a U-Net architecture.

[U-Net architecture]

Table of Contents

• U-Net Denoising Diffusion Probabilistic Model (DDPM)
  • Model Performance
    • Diffusion Process
    • Training Progress
    • Gradual Denoising Process
    • Heavy Noise Recovery
  • Architecture
    • U-Net Model
    • Diffusion Process
  • Model Components
    • Time Embedding
    • Residual Blocks
    • U-Net Structure

Model Performance

Diffusion Process

Training Progress

Training loss

Gradual Denoising Process

Progressive denoising steps demonstrating the model's ability to recover image details

Heavy Noise Recovery

Recover images from heavily corrupted inputs

Note: The model demonstrates pattern recognition capabilities, though its full potential is limited by computational resources. With increased computing power, the model could achieve more refined results and faster convergence.

Architecture

U-Net Model

• Custom U-Net architecture optimized for diffusion models
• Time-conditional generation through sinusoidal time embeddings
• Residual blocks with group normalization and Swish activation
• Multi-scale feature processing with skip connections
• Configurable channel multipliers and attention layers

Diffusion Process

• Denoising diffusion probabilistic model (DDPM)
• Linear noise schedule with configurable parameters
• Forward and reverse diffusion processes
• Stochastic sampling with learned noise prediction

Model Components

Time Embedding

• Sinusoidal positional encoding
• Multi-layer perceptron for time step processing
• Swish activation for non-linear transformations

Residual Blocks

• Group normalization for stable training
• Time-conditional convolutions
• Skip connections for gradient flow
• Swish activation functions

U-Net Structure

• Downsampling path with residual blocks
• Middle block for feature processing
• Upsampling path with skip connections
• Final convolution for image reconstruction