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Right set of UNet hyperparameters when training DDPM

See original GitHub issue

Hi there ! I am currently training a DDPM model on a custom image dataset following the cool unconditional_image_generation example script.

Since I don’t have the compute to perform comprehensive hyperparameter tuning of my architecture, I was wondering if there are any common intuitions when designing the UNet denoiser : width/length of the residual blocks, number and positions of the attention blocks, etc. with respect to the number of samples in the training set as well as their resolution.

If anyone has a wide experience in training DMs, it would be super cool to share insights here or in a dedicated blog post such as the one discussing the hyperparameters choice when training Dreambooth.

Thank you ! 🤗

Issue Analytics

State:
Created 10 months ago
Comments:5 (3 by maintainers)

Top GitHub Comments

1reaction

patrickvonplatencommented, Nov 18, 2022

Maybe cc @anton-l ?

0reactions

leopoldmaillardcommented, Dec 1, 2022

Hello @anton-l, thank you for your insight !

I also found out that Dhariwal & Nichol discussed hyperparameters tuning of DDPM in their paper Diffusion Models Beat GANs on Image Synthesis.

Will close this for now !

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