Diffusion Models From Scratch | Score-Based Generative Models Explained | Math Explained

Video Description

In this video we are looking at Diffusion Models from a different angle, namely through Score-Based Generative Models, which arguably can be considered as the broader family of diffusion models. Personally, this approach has helped me so much in getting a better intuition for diffusion models and how to visualize the idea and especially connect different approaches like DDPM, DDIM or EDM to one another. 00:00 Introduction 03:13 Score 04:18 Score Matching 09:10 Noise Perturbation 12:33 Denoising Score Matching 21:41 Sampling 24:00 Multiple Noise Perturbations 26:03 Differential Equations 31:36 Link to diffusion models 33:58 Summary 37:10 Conclusion Further Reading: 1. Sliced Score Matching: https://arxiv.org/pdf/1905.07088 2. Improved Techniques for Score-Based Generative Models: https://arxiv.org/pdf/2006.09011 3. Generative Modeling by Estimating Gradients of the Data Distribution: https://arxiv.org/pdf/1907.05600 4. Original Score Matching Paper (Hyvärinen): https://core.ac.uk/download/pdf/82826666.pdf 5. Langevian Dynamics: https://en.wikipedia.org/wiki/Langevin_dynamics 6. Score-Based Generative Modeling through Stochastic Differential Equations: https://arxiv.org/pdf/2011.13456 7. A Connection Between Score Matching and Denoising Autoencoders: https://www.iro.umontreal.ca/~vincentp/Publications/smdae_techreport.pdf 8. EDM: https://arxiv.org/pdf/2206.00364 9. DDPM: https://arxiv.org/pdf/2006.11239 10. DDIM: https://arxiv.org/pdf/2010.02502 11. Yang Song Blog Post on Score Matching: https://yang-song.net/blog/2021/score/ #diffusion #scorematching #stablediffusion #maths #flux #generativemodels