Summary of Generative Diffusion Modeling: a Practical Handbook, by Zihan Ding et al.
Generative Diffusion Modeling: A Practical Handbook
by Zihan Ding, Chi Jin
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The handbook provides a unified perspective on diffusion models, covering various types such as diffusion probabilistic models, score-based generative models, consistency models, and rectified flow. It aims to bridge the gap between paper and code by standardizing notations and providing implementations. The content includes fundamentals of diffusion models, pre-training process, and post-training methods like model distillation and reward-based fine-tuning. This practical guide focuses on widely adopted approaches in generative modeling with diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This handbook helps us understand how to use special kinds of computer models called “diffusion models.” It’s a big book that tries to make it easier for people to use these models by showing how they work and giving examples. The book covers the basics, like what these models are and how they’re made, as well as ways to improve them once they’re built. It’s written in a way that makes it easy to follow along, even if you don’t know much about computers or math. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Fine tuning
