There are several resources to understand how to make a GAN from scratch. But with Diffusion Models, the DDPM paper code and especially the Improved DDPM paper code are so hard and complicated to understand.

I'm currently doing research on Diffusion Models. I understand the math very well from the paper: "Understanding Diffusion Models: A Unified Perspective". It gives a very intuitive and step by step guide to the mathematics and intuition behind Diffusion Models. I loved the paper. But from that paper, I don't have the necessary talent or skill to reproduce the code. It's too big of a project for me to do on my own from scratch.

So, I wanted help from this community which can give me some guide on how to find articles, GitHub repos or YouTube videos which gives me step by step guide on how to build research level code on Diffusion models from scratch.

The closest I found was the annotated diffusion model from the Huggingface community but that was very basic and when I wanted to reproduce their own repo on GitHub(annotated diffusion is the name of the blog but they have their own repo on Diffusion model in Pytorch which is in research level) in Pytorch, that was too heavy for me.

Also, maybe some other guides maybe also helpful on how can I can start from scratch and build models and eventually go to a stage where I can reproduce results like in the Improved DDPM paper.

I have the hardware resources in my laboratory. But no one in my lab has done any projects on Diffusion Models. So, I'm the first one. That's why I had to find resources on my own.

It will be a big help for me if I can find some help from this community. Thank you very much in advance.

Comments

yabik t1_j0r8dxp wrote on December 18, 2022 at 8:31 PM

#964,585

https://www.youtube.com/watch?v=a4Yfz2FxXiY This video might be a starting point. I also look for a more updated version.

Itachi_99 OP t1_j0rb5hb wrote on December 18, 2022 at 8:50 PM

#964,737

Replying to bhakt_chungus (#964,549)

Thank you so much for the help. I hope this will help a bit. I need to understand the codes of each section very carefully. Gotta revise the theory also.

Itachi_99 OP t1_j0rbeey wrote on December 18, 2022 at 8:51 PM

#964,748

Replying to yabik (#964,585)

Thank you very much. I watched the video previously. But I wanted a more robust and updated video for a research level architecture and code explanation. But this is also helpful. Thank you! Kind stranger.

FyreMael t1_j0rdyv5 wrote on December 18, 2022 at 9:08 PM

#964,864

Annotated PyTorch Paper Implementations:

https://nn.labml.ai/diffusion/ddpm/index.html

eigenfudge t1_j0rt2ea wrote on December 18, 2022 at 10:54 PM

#965,533

This tutorial is really nice and digestible to follow, gives background and builds up nicely to the DDPM-type diffusion model:

https://github.com/acids-ircam/diffusion_models

pilooch t1_j0tjsw7 wrote on December 19, 2022 at 8:26 AM

#968,837

Hello, the goto tutorial I do recommend around to colleagues and customers/researchers is the one from CVPR 2022: https://cvpr2022-tutorial-diffusion-models.github.io/ Some do skip the score-based presentations, and/or start from the applications instead. Very informative in all cases !

Tea_Pearce t1_j0tvm4v wrote on December 19, 2022 at 11:18 AM

#969,415

This repo is a single minimal script that does conditional generation on MNIST: https://github.com/TeaPearce/Conditional_Diffusion_MNIST

Chrex_007 t1_j0txkmh wrote on December 19, 2022 at 11:43 AM

#969,528

Thanks, this helped me too.

sog_unit t1_j0uv5iq wrote on December 19, 2022 at 4:28 PM

#971,827

Lucidrains GitHub is always my go to repo for understandable paper implementations https://github.com/lucidrains/denoising-diffusion-pytorch

[deleted] t1_j0xktib wrote on December 20, 2022 at 3:52 AM

#978,651

[removed]

[D] Resources to learn and fully understand Diffusion Model Codes