soundstorm-pytorch VS parti-pytorch

Compare soundstorm-pytorch vs parti-pytorch and see what are their differences.

soundstorm-pytorch

Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch (by lucidrains)

parti-pytorch

Implementation of Parti, Google's pure attention-based text-to-image neural network, in Pytorch (by lucidrains)
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soundstorm-pytorch parti-pytorch
1 2
1,122 506
- -
7.3 5.5
12 days ago 5 months ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.

soundstorm-pytorch

Posts with mentions or reviews of soundstorm-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-19.

parti-pytorch

Posts with mentions or reviews of parti-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-22.
  • Google Parti open source implementation
    1 project | news.ycombinator.com | 27 Jun 2022
  • Pathways Autoregressive Text-to-Image Model (Parti)
    2 projects | news.ycombinator.com | 22 Jun 2022
    Give it a few days and lucidrains will have the code up[0].

    But in honesty, it is probably how people react. We saw this with Pulse, GPT, and many others. The authors are clear about the limitations but people talk it up too much and others shit on it. There's also a reproducibility crisis in ML (many famous networks, like Swin[1][2][3], can't be reproduced (even worse when reviewers concentrate on benchmarks)). It isn't like many can train a model like this anyways. It gives them benefit of the doubt and maintains good publicity rather than controversial.

    Of course, this is extremely bad from an academic perspective and personally I believe you should have your paper revoked if it isn't reproducible. You'd be surprised how many don't track the random seed or measure variance. We have GitHub. You should be able to write training options that get approximately the same results as the paper. Otherwise I don't trust your results.

    [0] https://github.com/lucidrains/parti-pytorch

    [1] https://github.com/microsoft/Swin-Transformer/issues/183

    [2] https://github.com/microsoft/Swin-Transformer/issues/180

    [3] https://github.com/microsoft/Swin-Transformer/issues/148

What are some alternatives?

When comparing soundstorm-pytorch and parti-pytorch you can also consider the following projects:

audio-diffusion-pytorch - Audio generation using diffusion models, in PyTorch.

Swin-Transformer - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

slot-attention - Implementation of Slot Attention from GoogleAI

DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

tortoise-tts-fast - Fast TorToiSe inference (5x or your money back!)

muse-maskgit-pytorch - Implementation of Muse: Text-to-Image Generation via Masked Generative Transformers, in Pytorch

voicebox - Reskinning the pink trombone tract synth

deep-daze - Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun

word2wave - Word2Wave: a framework for generating short audio samples from a text prompt using WaveGAN and COALA.

performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch

deep-implicit-attention - Implementation of deep implicit attention in PyTorch

x-transformers - A simple but complete full-attention transformer with a set of promising experimental features from various papers