Stable Diffusion Implementation from Scratch

This project implements Stable Diffusion from scratch, focusing on learning and understanding each component. The implementation follows an incremental approach, starting with basic components and gradually adding advanced features.

Project Roadmap

Phase 1: Core Components

1. Basic Framework Setup

   • Set up project structure
   • Implement basic data loading and preprocessing
   • Create training utilities

2. U-Net Implementation

   • Basic U-Net architecture
   • Time embedding
   • Cross-attention mechanisms
   • Residual connections

3. DiT (Diffusion Transformer) Implementation

   • Transformer blocks
   • Self-attention layers
   • Position embeddings
   • Integration with diffusion process

4. Diffusion Process

   • Forward diffusion process
   • Reverse diffusion process
   • Noise scheduling
   • Basic sampling methods

Phase 2: Training Pipeline

1. Loss Functions

   • MSE loss implementation
   • Noise prediction objectives
   • VLB (Variational Lower Bound) components

2. Training Loop

   • Batch processing
   • Gradient computation
   • Model optimization
   • Validation metrics

3. Basic Text Conditioning

   • Text encoder integration
   • Cross-attention mechanisms
   • Text-image alignment

Phase 3: Advanced Features

1. Textual Inversion

   • Token learning
   • Embedding optimization
   • Concept preservation

2. Performance Optimizations

   • Memory efficiency improvements
   • Training speed optimizations
   • Inference optimizations

3. Advanced Sampling Techniques

   • DDIM sampling
   • DPM-Solver
   • Classifier-free guidance

Phase 4: Extensions

1. Advanced Conditioning

   • Image conditioning
   • Multiple condition fusion
   • Control mechanisms

2. Model Improvements

   • Architecture refinements
   • Advanced attention mechanisms
   • Improved scheduling strategies

Project Structure

stable-diffusion/
├── src/
│   ├── models/          # Core model implementations
│   ├── diffusion/       # Diffusion process logic
│   ├── training/        # Training utilities
│   └── utils/           # Helper functions
├── configs/             # Configuration files
├── scripts/             # Training and inference scripts
└── data/                # Dataset management

Getting Started

[To be implemented]

Requirements

[To be implemented]

References

• "High-Resolution Image Synthesis with Latent Diffusion Models" (Rombach et al.)
• "DiT: Self-supervised Pre-training for Document Image Transformer" (Li et al.)
• "Understanding Diffusion Models: A Unified Perspective" (Luo et al.)