The Annotated Diffusion Model

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The Annotated Diffusion Model explains the concepts and implementation of diffusion models in deep learning

advanced Published 7 Jun 2022
Action Steps
  1. Understand the definition and mathematical formulation of diffusion models
  2. Learn about the neural network architecture used in diffusion models, including ResNet blocks, attention modules, and group normalization
  3. Implement the forward diffusion process using a conditional U-Net
  4. Experiment with the annotated diffusion model using the provided Colab notebook
Who Needs to Know This

AI engineers and researchers can benefit from this article to understand the mathematical formulation and implementation of diffusion models, which can be applied to various tasks such as image and video generation

Key Insight

💡 Diffusion models are a class of generative models that can be used for image and video generation, and understanding their mathematical formulation and implementation is crucial for applying them to real-world tasks

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🤖 Learn about diffusion models and their implementation in deep learning with The Annotated Diffusion Model #AI #DeepLearning

Key Takeaways

The Annotated Diffusion Model explains the concepts and implementation of diffusion models in deep learning

Full Article

# The Annotated Diffusion Model

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# [](https://huggingface.co/blog/annotated-diffusion#the-annotated-diffusion-model) The Annotated Diffusion Model

Published June 7, 2022

[Update on GitHub](https://github.com/huggingface/blog/blob/main/annotated-diffusion.md)

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* [What is a diffusion model?](https://huggingface.co/blog/annotated-diffusion#what-is-a-diffusion-model "What is a diffusion model?")

* [In more mathematical form](https://huggingface.co/blog/annotated-diffusion#in-more-mathematical-form "In more mathematical form")

* [Defining an objective function (by reparametrizing the mean)](https://huggingface.co/blog/annotated-diffusion#defining-an-objective-function-by-reparametrizing-the-mean "Defining an objective function (by reparametrizing the mean)")

* [The neural network](https://huggingface.co/blog/annotated-diffusion#the-neural-network "The neural network")
* [Network helpers](https://huggingface.co/blog/annotated-diffusion#network-helpers "Network helpers")

* [Position embeddings](https://huggingface.co/blog/annotated-diffusion#position-embeddings "Position embeddings")

* [ResNet block](https://huggingface.co/blog/annotated-diffusion#resnet-block "ResNet block")

* [Attention module](https://huggingface.co/blog/annotated-diffusion#attention-module "Attention module")

* [Group normalization](https://huggingface.co/blog/annotated-diffusion#group-normalization "Group normalization")

* [Conditional U-Net](https://huggingface.co/blog/annotated-diffusion#conditional-u-net "Conditional U-Net")

* [Defining the forward diffusion process](https://huggingface.co/blo
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