evodiff
Generation of protein sequences and evolutionary alignments via discrete diffusion models (by microsoft)
denoising-diffusion-pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch (by lucidrains)
evodiff | denoising-diffusion-pytorch | |
---|---|---|
1 | 11 | |
425 | 7,075 | |
5.9% | - | |
8.9 | 8.5 | |
about 1 month ago | 8 days ago | |
Python | Python | |
MIT License | MIT License |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
evodiff
Posts with mentions or reviews of evodiff.
We have used some of these posts to build our list of alternatives
and similar projects.
-
What’s Next in Protein Design? Microsoft Researchers Introduce EvoDiff: A Groundbreaking AI Framework for Sequence-First Protein Engineering
Github: https://github.com/microsoft/evodiff
denoising-diffusion-pytorch
Posts with mentions or reviews of denoising-diffusion-pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-30.
- 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!