[D] Why is the diffution model so powerful? but the math behind it is so simple.

This page summarizes the projects mentioned and recommended in the original post on /r/MachineLearning

Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
  • minDiffusion

    Self-contained, minimalistic implementation of diffusion models with Pytorch.

  • You can see the 200 lines code here: https://nn.labml.ai/diffusion/ddpm/index.html and https://github.com/cloneofsimo/minDiffusion, math is here: https://lilianweng.github.io/posts/2021-07-11-diffusion-models/

  • score_sde

    Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

  • Turns out that diffusion models also define a certain differential equation, making it a neural ODE. Then you can just integrate the ODE in the other direction to get the exact inverse for the DDPM (it's not entirely exact b/c of numerical error in the solver, but close enough)

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

    WorkOS logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts