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Improvement suggestions for the tutorial on flow matching

Open kilianFatras opened this issue 1 year ago • 10 comments

This issue is opened for users to suggest improvements for the Flow Matching tutorial notebook.

kilianFatras avatar Dec 15 '23 19:12 kilianFatras

Is it possible to include a minimal example of how a trained model can be used to evaluate the density of a given sample?

radiradev avatar Dec 22 '23 16:12 radiradev

@radiradev Thats a great idea. I can work on this. If you have an immediate need or are interested in making one I can send over a dirty notebook for this.

atong01 avatar Dec 22 '23 17:12 atong01

@atong01 That would be very much appreciated!

radiradev avatar Dec 23 '23 14:12 radiradev

We should give credit to stochastic interpolants and rectified flows at the beginning of the notebook. Basically saying that all 3 methods are similar and concurrent ICLR2023 papers

kilianFatras avatar Jan 13 '24 06:01 kilianFatras

Hi, there is a typo in Flow_matching_tutorial.ipynb: "optximizer.zero_grad()" ->"optimizer.zero_grad()"

hinzflorian avatar Feb 13 '24 15:02 hinzflorian

Hi, Thank you! I will correct that shortly.

kilianFatras avatar Feb 13 '24 16:02 kilianFatras

Is it possible to include a minimal example of how a trained model can be used to evaluate the density of a given sample?

Hi @atong01, could you please share your example notebook on how this is done? I saw here that to evaluate the density an ode must be solved, but I am not sure how to accomplish this in pytorch:

jax-fmx

radiradev avatar Feb 22 '24 16:02 radiradev

Hello,

I cannot run the last cell in the Flow_matching_tutorial.ipynb notebook because the sample_xt function is not define. Does sample_xt serve the same purpose as sample_conditional_pt? Are the two functions interchangable?

Thank you for the tutorials!

harveymannering avatar Mar 08 '24 12:03 harveymannering

Oh that’s indeed a typo… I changed the name of the function last minute and forgot to change this. You are right! The correct function is sample_conditional_pt.

Edit: I have pushed a corrected tutorial.

kilianFatras avatar Mar 08 '24 14:03 kilianFatras

We should give credit to stochastic interpolants and rectified flows at the beginning of the notebook. Basically saying that all 3 methods are similar and concurrent ICLR2023 papers

Your suggestion is fantastic, I'm a beginner and it looks like there is no difference between stochastic interpolants and rectified flows, except that stochastic interpolants have an extra random term and rectified flows seem to be a deterministic sampling, is my understanding Is my understanding correct? I look forward to your reply, thanks!

csufangyu avatar May 12 '24 03:05 csufangyu