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1
Introduction
2
Issues with standard diffusion models
3
Visualizing the issue with data
4
Method - Reconstruction Loss
5
Method - Adversarial Loss
6
Method - Conditioning
7
Experiments
8
Image Generation with Unconditional Latent Diffusion
9
Super-Resolution with Latent Diffusion
10
A person crossing a busy intersection
11
Conclusion
12
Points For the Paper
13
Points Against the Paper
Description:
Explore the innovative Stable Diffusion model in this 30-minute lecture from the University of Central Florida. Delve into the challenges of standard diffusion models, visualize data issues, and examine key methods including reconstruction loss, adversarial loss, and conditioning. Discover experiments in unconditional latent diffusion for image generation and super-resolution techniques. Analyze a real-world scenario of a person crossing a busy intersection. Conclude with a critical evaluation of the paper's strengths and weaknesses, gaining valuable insights into this cutting-edge machine learning approach.

Stable Diffusion

University of Central Florida
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