🚀 Diffusion and Multimodal Models Implementation

This repository contains implementations of state-of-the-art diffusion models and multimodal architectures, focusing on the theoretical foundations and practical applications of these cutting-edge AI technologies.

Overview

This project explores the implementation of diffusion-based generative models and multimodal architectures like CLIP (Contrastive Language-Image Pre-training). The codebase provides a comprehensive, from-scratch implementation of these models, demonstrating a deep understanding of the underlying mathematical principles and architectural designs.

Key Components

Diffusion Models

• Denoising Diffusion Probabilistic Models (DDPM): Implementation of the core DDPM algorithm, including:

  • Forward diffusion process (adding noise gradually)
  • Reverse denoising process (removing noise step by step)
  • Variance scheduling techniques
  • Sampling methods

• Denoising Diffusion Implicit Models (DDIM): Implementation of the DDIM sampling technique for faster generation with fewer steps.

• U-Net Architecture: A sophisticated neural network architecture used in diffusion models, featuring:

  • Downsampling and upsampling blocks
  • Skip connections
  • Self-attention mechanisms
  • Residual blocks
  • Group normalization
  • Sinusoidal time embeddings

Multimodal Models

• CLIP (Contrastive Language-Image Pre-training): Implementation of OpenAI's CLIP model, including:
  • Vision transformer for image encoding
  • Text transformer for text encoding
  • Contrastive learning approach
  • Zero-shot capabilities

Guidance Techniques

• Classifier Guidance: Implementation of classifier-guided diffusion for conditional generation. (WIP)
• Classifier-Free Guidance: Implementation of classifier-free guidance for unconditional generation. (WIP)

💡 Technical Skills Demonstrated

• Deep Learning Frameworks: PyTorch implementation with advanced tensor operations
• Neural Network Architectures: Transformers, U-Net, CNNs, attention mechanisms
• Mathematical Foundations: Probabilistic modeling, stochastic differential equations
• Computer Vision: Image processing, feature extraction, generative modeling
• Natural Language Processing: Text embeddings, cross-modal learning
• Software Engineering: Modular code design, testing frameworks, experiment tracking

🛠️ Tools and Technologies

• PyTorch: Core deep learning framework
• einops: For elegant tensor operations
• Weights & Biases (wandb): For experiment tracking and visualization
• matplotlib: For visualization of images and model outputs
• tqdm: For progress tracking during training and inference

🖼️ Applications

The models implemented in this repository can be used for various applications:

• Text-to-image generation
• Image editing and manipulation
• Style transfer
• Data augmentation
• Zero-shot image classification
• Multimodal understanding

📚 References

• ML for Alignment Bootcamp (MLAB) - https://github.com/Kiv/mlab2
• Denoising Diffusion Probabilistic Models - https://arxiv.org/pdf/2006.11239
• Denoising Diffusion Implicit Models - https://arxiv.org/pdf/2010.02502
• Learning Transferable Visual Models From Natural Language Supervision - https://arxiv.org/pdf/2103.00020