Beyond Diffusion: Flow Matching for Generative AI

Flow Matching: A Simpler and Faster Approach to Generative Modeling

As generative AI systems continue to advance, traditional diffusion models often introduce complexity through iterative denoising and carefully tuned noise schedules. Flow Matching offers a more intuitive alternative by learning continuous transformations that move noise directly toward data, enabling faster sampling and simpler training.

In this session, we’ll introduce the fundamentals of Flow Matching and explain how it differs from diffusion-based approaches in both theory and practice. You’ll gain a clear understanding of why this method is gaining traction in state-of-the-art systems such as Stable Diffusion 3 and Meta’s Movie Gen, and where it fits in the broader generative AI landscape.

Through visual explanations and a hands-on code walkthrough, we’ll demonstrate how a model can be trained from scratch to transport noise into structured data. By the end of the session, you’ll have a solid conceptual foundation and practical starting points for exploring this approach in your own generative AI projects.

🛠️ What We’ll Cover:

• Core Concepts – An intuitive explanation of Flow Matching and how it compares to diffusion models
• Modeling Approaches Compared – Strengths, trade-offs, and why straight transport paths improve efficiency
• Real-World Use Cases – Applications in image generation, video synthesis, audio modeling, and molecular design
• Key Mechanics – Velocity fields, continuous flows, and simplified training objectives
• Hands-On Demo – A step-by-step notebook walkthrough using a toy dataset with visualized particle movement
• Efficiency Benefits – Reduced sampling steps, simpler objectives, and maintained output quality
• Getting Started – Recommended papers, tools, and libraries to begin experimenting

🔍 Why Attend?

• Understand modern generative modeling beyond traditional diffusion methods
• Learn how newer approaches improve efficiency without added complexity
• See practical, code-driven explanations instead of purely theoretical slides
• Leave with clear guidance and resources you can apply immediately