Faster Diffusion - presentation of the Denoising Diffusion Implicit Models paper

Here, we talk about Denoising Diffusion Implicit Models, a kind of diffusion models introduced by Song and al (2021)
This variation leads to a shorter sampling time compared to the original Denoising Diffusion Probabilistic Model (Ho and al, 2020), among other interesting properties.
Link to the paper : openreview.net...

[@MonkkSoori]

This was an excellent overview and explanation of DDIM after I learned about DDPM. Thank you.

[@talktovipin1]

Nice explanation. Looking forward for similar explanation on Classifier-Free Diffusion Guidance.

[@won20529jun]

What a brilliant explanation. Thank you so much!!!

[@mehershashwatnigam5581]

Good high level explanation!

[@frederictost6659]

Super vidéo merci Tidiane

[@vivekmittal9080]

Interesting talk, very well explained. Thank you :)

[@clemacort5151]

Great explanation, thank you very much !

[@charlherbst4583]

If you make the sampling deterministic, set eta=0, do you just generate the same training data? Does eta>0 to say you generated novel images?

[@TheAero]

I may understand diffusion at high level, however the math just seem kinda random.

[@r00t257]

excellent explanation

[@1HARVEN1]

Thanks for the video

[@DeQuinceyBenedict]

4738 Morar Forest

[@zhshen7981]

Nice Viedo