Diffusion Models From Scratch | Score-Based Generative Models Explained | Math Explained
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/82826...
5. Langevian Dynamics: https://en.wikipedia.org/wiki/Langevi...
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/~vincent...
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
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