denoising-diffusion-pytorch VS autoregressive

I’m trying to train a diffusion model using this code (https://github.com/lucidrains/denoising-diffusion-pytorch). My idea is to take a short audio segment, transform it into a spectrogram and train the model on these images then have it generate spectrograms then go back to audio. However the model requires square images. I cannot for the life of me figure out how to make a square spectrogram. Also is a regular spectrogram or a mel spectrogram better for this application?

Generally it's without weights, but MusicLM is also a WIP more mature implementations have descriptions on how to train them and follow ups on small scale/crowd-sourced experiments & research[1].

[1]: https://github.com/lucidrains/denoising-diffusion-pytorch

[1] https://colab.research.google.com/drive/1sjy9odlSSy0RBVgMTgP7s99NXsqglsUL?usp=sharing#scrollTo=KOYPSxPf_LL7 [2] https://github.com/lucidrains/denoising-diffusion-pytorch/blob/main/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

Sure. I am using: https://github.com/lucidrains/denoising-diffusion-pytorch

Lucidrains GitHub is always my go to repo for understandable paper implementations https://github.com/lucidrains/denoising-diffusion-pytorch

Thanks for the reply. Is there any suggestion if I wanted to train a model to generate half cat and half butterfly images what I should do? I git cloned the code from https://github.com/lucidrains/denoising-diffusion-pytorch and trained from scratch.

DDIM/DDPM are the same model to train, they only differ at inference time. To start I would recommend building from lucidrains' MIT licenced version (https://github.com/lucidrains/denoising-diffusion-pytorch). Just play around with the models until you gain an intuition.

Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!

Once you understand these papers you can begin to understand Palette, and from there I would start with an open-source diffusion implementation like this one and then modify it to suit your needs!