denoising-diffusion-pytorch
audiolm-pytorch
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| denoising-diffusion-pytorch | audiolm-pytorch | |
|---|---|---|
| 11 | 4 | |
| 6,994 | 2,242 | |
| - | - | |
| 8.6 | 9.0 | |
| 14 days ago | 3 months ago | |
| Python | Python | |
| MIT License | MIT License |
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denoising-diffusion-pytorch
- Commits · lucidrains/denoising-diffusion-pytorch
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Help using torchaudio and spectrograms for diffusion
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?
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Implementation of Google's MusicLM in PyTorch
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
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[D] Time Embedding in Diffusion Model
[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
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[D] Can a Diffusion Model be trained with an NVIDIA TITAN X?
Sure. I am using: https://github.com/lucidrains/denoising-diffusion-pytorch
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[D] Resources to learn and fully understand Diffusion Model Codes
Lucidrains GitHub is always my go to repo for understandable paper implementations https://github.com/lucidrains/denoising-diffusion-pytorch
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Diffusion model generated exactly the same image as the training image
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.
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[D] Best diffusion model archetype to train?
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.
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We just release a complete open-source solution for accelerating Stable Diffusion pretraining and fine-tuning!
Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!
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[D] Introduction to Diffusion Models
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!
audiolm-pytorch
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Bark: A transformer based text to audio system
It’s mostly there in https://github.com/lucidrains/audiolm-pytorch#hierarchical-t....
- FLiPN-FLaNK Stack Weekly 27Feb2023
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Implementation of Google's MusicLM in PyTorch
This one is AudioLM modified from here https://github.com/lucidrains/audiolm-pytorch repository to support the music generation needs here.
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Microsoft’s new text-to-speech model can duplicate anyone's voice in 3 seconds
There is an open source implementation of these features in Pytorch:
https://github.com/lucidrains/audiolm-pytorch
What are some alternatives?
ALAE - [CVPR2020] Adversarial Latent Autoencoders
bark - 🔊 Text-Prompted Generative Audio Model
autoregressive - :kiwi_fruit: Autoregressive Models in PyTorch.
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
highlight - highlight.io: The open source, full-stack monitoring platform. Error monitoring, session replay, logging, distributed tracing, and more.
Awesome-Diffusion-Models - A collection of resources and papers on Diffusion Models
musiclm-pytorch - Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch
RAVE - Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
iTransformer - Unofficial implementation of iTransformer - SOTA Time Series Forecasting using Attention networks, out of Tsinghua / Ant group
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
jxc - JXC is a structured data language similar to JSON, but with a focus on being expressive, extensible, and human-friendly.