The document discusses denoising diffusion probabilistic models, highlighting their advantages over GANs, such as fine-grained control and reduced risk of mode collapse, although they currently have slower sampling times. It also covers various probability distributions and their associations, as well as applications in generative modeling across industries like graphic design, film, and gaming. Key examples of diffusion tools mentioned include DALL-E 3 and Stable Diffusion.

1. Denoising Diffusion
Probabilistic Models
AI and ML Team
Shriram (Head)
Samyuktaa
Sanjai Balajee
Shreyas Sai
Sushmithaa P

2. Image in Math Terms

3. Probability Distribution
Some of the Common Distributions:
→ Uniform
→ Bernoulli
→ Binomial
→ Normal
→ Poisson
→ Chi Squared
Associated Terms: PMF, PDF, CDF

4. Image as a distribution

5. Transport Maps
Types of Modelling

6. Generative Modelling

7. Transfer Modelling

8. Optimal Transport Problem

9. What can we learn from
Thermodynamics

10. Standard Brownian Motion

11. Langevin Dynamics

12. Fokker Plank Equation

13. Noising and the Forward Process

14. Denoising and the Reverse Process

15. Connection with Stochastic Gradient Langevin Dynamics

16. Parametrization

17. Why are Diffusion models better than
GANS?
● Diffusion Models offer fine-grained control over the generation process
● GANs may suffer from mode collapse, where the generator produces
limited or repetitive samples
● Currently they are still slower than GANs at sampling time due to the use
of multiple denoising steps

18. Diffusion Tools
● DALL-E 3: Developed by OpenAI
● Stable Diffusion: Developed by Stability AI

19. ● Graphic design
● Film and animation
● Music and sound design
● Media and gaming industry
Use Cases

20. → Flow matching for generative modelling, Lipman et.al
→ Flow straight and fast: learning to generate and transfer data with rectified
flow. Liu et. al
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