Diffusion Models | PyTorch Implementation
Diffusion Models are generative models just like GANs. In recent times many state-of-the-art works have been released that build on top of diffusion models such as #dalle , #imagen or #stablediffusion . In this video I'm coding a PyTorch implementation of diffusion models in a very easy and straightforward way. At first I'm showing how to implement an unconditional version and subsequently train it. After that I'm explaining 2 popular improvements for diffusion models: classifier free guidance and exponential moving average. I'm also going to implement both updates and train a conditional model on CIFAR-10 and afterwards compare the different results.
Code: https://github.com/dome272/Diffusion-...
#diffusion #dalle2 #dalle #imagen #stablediffusion
00:00 Introduction
02:05 Recap
03:16 Diffusion Tools
07:22 UNet
13:07 Training Loop
15:44 Unconditional Results
16:05 Classifier Free Guidance
19:16 Exponential Moving Average
21:05 Conditional Results
21:51 Github Code & Outro
Further Reading:
1. Paper: https://arxiv.org/pdf/1503.03585.pdf
2. Paper: https://arxiv.org/pdf/2006.11239.pdf
3. Paper: https://arxiv.org/pdf/2102.09672.pdf
4. Paper: https://arxiv.org/pdf/2105.05233.pdf
5. CFG: https://arxiv.org/pdf/2207.12598.pdf
6. Timestep Embedding: https://machinelearningmastery.com/a-...
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