# Tutorial on Diffusion Models for Imaging and Vision

**Authors:** Stanley H. Chan  
**arXiv:** [2403.18103](https://arxiv.org/abs/2403.18103)  
**Date:** 2024-03-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.18103v1/

## Key points

- Diffusion models have seen significant growth in generative tools, particularly in text-to-image and text-to-video generation.
- These generative tools are powered by the concept of diffusion, which has addressed previous shortcomings in image and video generation approaches.
- The tutorial aims to discuss essential ideas behind diffusion models for undergraduate and graduate students interested in researching or applying them.
- Key takeaways include deriving diffusion ideas from various perspectives like VAE, DDPM, SMLD, and SDE.
- Emphasis is placed on denoising diffusion's small increment, a key aspect not previously recognized during the GANs and VAEs era.
- Speed remains a challenge due to the incremental nature of diffusion models despite efforts in knowledge distillation to improve it.
- Questions are raised about generating noise from non-Gaussian distributions and exploring applications of diffusion models in inverse problems like image restoration using existing solvers.

## Summary

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The tutorial on Diffusion Models for Imaging and Vision delves into the astonishing growth of generative tools in recent years, particularly in text-to-image generation and text-to-video generation. These generative tools are powered by the concept of diffusion, a sampling mechanism that has overcome previous shortcomings in approaches to image and video generation. The tutorial aims to discuss the essential ideas underlying diffusion models, catering to undergraduate and graduate students interested in researching diffusion models or applying them to solve various problems. The tutorial covers fundamental concepts behind the development of diffusion-based generative models in recent literature. It emphasizes describing these foundational ideas rather than relying solely on Python demos due to the vast and rapidly expanding literature on the subject. Key takeaways from the tutorial include insights into deriving the same diffusion idea independently from various perspectives such as VAE, DDPM, SMLD, and SDE. It also highlights the significance of denoising diffusion's small increment, which was not previously recognized during the era of GANs and VAEs. While iterative denoising is currently considered state-of-the-art, it may not be the ultimate solution as humans do not generate images from pure noise. Additionally, speed remains a major challenge due to the incremental nature of diffusion models, despite efforts in knowledge distillation to address this issue. The tutorial also raises questions about generating noise from non-Gaussian distributions and explores applications of diffusion models in inverse problems like image restoration using existing solvers like Plug-and-Play ADMM algorithm with an explicit diffusion sampler. Overall, this comprehensive tutorial provides valuable insights into diffusion models for imaging and vision, offering a deeper understanding of their principles and potential applications in research and problem-solving scenarios.