Latent Diffusion Model for Image Generation

An implementation of a Latent Diffusion Model designed for image generation. This repository contains a PyTorch-based model that combines ResNet blocks, self-attention mechanisms, and downsampling/upsampling operations to learn latent representations for generating high-quality images.

What is a Latent Diffusion Model?

A Latent Diffusion Model (LDM) is a generative model that learns to generate data by gradually denoising it in a latent (lower-dimensional) space. Unlike standard diffusion models that operate directly in pixel space, LDMs work in a compressed latent space for improved computational efficiency and generation quality. The process involves:

1. Forward Process: Gradually adding noise to the input data.
2. Reverse Process: Learning to reverse the noise addition through a neural network.
3. Latent Space: Performing these operations in a compressed latent space to reduce computational complexity.

Key Components

• ResNet Blocks: Capture local spatial features through convolutional layers.
• Self-Attention Blocks: Model long-range dependencies in the data.
• Downsampling/Upsampling: Compress and expand spatial resolution while preserving features.
• Latent Conditioning: Integrates latent embeddings to guide the generation process.

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Mathematical Intuition

Diffusion models minimize the difference between predicted and actual noise at each denoising timestep. The loss function for a latent diffusion model can be simplified as:

Loss = 𝔼ₜ[||ε - εₜ(xₜ, t)||²]

Where:

• ε is the actual noise added to the input x at timestep t.
• εₜ is the noise predicted by the model.

The model learns to iteratively denoise the input by predicting the noise component.

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Repository Overview

Code Components

1. Core Modules

• model.py: Main Latent Diffusion Model architecture.
• ResNetBlock.py: Residual blocks with convolutional layers and group normalization.
• SelfAttentionBlock.py: Multi-head self-attention with layer normalization.
• DownSample.py: Downsampling operation with latent conditioning.
• UpSample.py: upsampling operation with latent conditioning.

2. Key Features

• Latent space conditioning at multiple resolutions
• Skip connections for preserving spatial features
• Multi-head attention for global context modeling

Architecture Details

Forward Pass Flow

1. Initial Processing: Input image passes through a ResNet block.
2. Downsampling: Three downsampling stages reduce spatial resolution while increasing channel depth.
3. Bottleneck: Self-attention blocks process features at the lowest resolution.
4. Upsampling: Three upsampling stages reconstruct the image using residual connections.
5. Final Convolution: Maps features back to original input channels.

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Installation

Install required dependencies:

pip install torch torchvision

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Usage

Training

1. Prepare dataset directory structure:

   dataset/
   ├── train/
   └── val/
   

Example Code

import torch
from model import LatentDiffusionModel

# Initialize model
model = LatentDiffusionModel(
    input_channels=3,
    latent_dim=512,
    image_size=(64, 64),
    num_heads=4
)

# Example input
x = torch.randn(8, 3, 64, 64)  # Batch of 8 RGB images
latent = torch.randn(8, 512)   # Random latent vectors

# Forward pass
output = model(x, latent)
print(f"Output shape: {output.shape}")  # (8, 3, 64, 64)

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Key Features

• ✅ Efficient latent space operations
• ✅ Multi-resolution self-attention
• ✅ Residual skip connections
• ✅ Flexible latent conditioning

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Future Improvements

• Implement time-step conditioning
• Add classifier-free guidance (cfg)
• Experiment with VAE-based latent spaces
• Add mixed-precision training

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References

1. Denoising Diffusion Probabilistic Models (2020)
2. High-Resolution Image Synthesis with Latent Diffusion Models (2021)
3. Attention Is All You Need (2017)