A Unified Theoretical Framework for the Synergistic Integration of Transformers and Diffusion Models

This paper introduces a novel, comprehensive theoretical framework for the synergistic integration of Transformer and Diffusion models, two paradigms that have independently revolutionized machine learning. We establish a fundamental correspondence between these models through a unified representation and a generalized dynamics equation, bridging the gap between their seemingly disparate architectures. Our key contributions include:

(1) A unified mathematical formulation that encapsulates both Transformer and Diffusion processes;

(2) A novel Diffusion-Enhanced Attention mechanism that incorporates Diffusion dynamics into Transformer attention;

(3) Rigorous theoretical analyses including convergence guarantees, generalization bounds, and sample efficiency proofs for the integrated model.

We provide detailed mathematical derivations and empirical validations across various tasks, demonstrating significant improvements over standalone models and existing hybrid approaches. This work lays the foundation for a new class of AI models that leverage the strengths of both paradigms, potentially leading to more powerful, efficient, and versatile AI systems. Our framework opens up new avenues for research in areas such as enhanced language modeling, advanced image generation, and multi-modal learning, paving the way for the next generation of AI technologies.