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 beckham.nz
Learning the conditional prior over classes for image diffusion
| | lilianweng.github.io
What are Diffusion Models? | Lil'Log
4.2 parsecs away

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| | [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [Updated on 2022-08-31: Added latent diffusion model. [Updated on 2024-04-13: Added progressive distillation, consistency models, and the Model Architecture section.
| | www.nicktasios.nl
Latent Diffusion Series: Latent Diffusion Model | NICK TASIOS
3.1 parsecs away

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| | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the third, and last, post,
| | sander.ai
The geometry of diffusion guidance Sander Dieleman
2.9 parsecs away

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| | More thoughts on diffusion guidance, with a focus on its geometry in the input space.
| | vxlabs.com
Variational Autoencoder in PyTorch, commented and annotated. - vxlabs
14.0 parsecs away

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| I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time.