pytorch-generative
Easy generative modeling in PyTorch. (by EugenHotaj)
vq-vae-2-pytorch
Implementation of Generating Diverse High-Fidelity Images with VQ-VAE-2 in PyTorch (by rosinality)
| pytorch-generative | vq-vae-2-pytorch | |
|---|---|---|
| 1 | 2 | |
| 403 | 1,438 | |
| - | - | |
| 3.4 | 0.0 | |
| 8 months ago | about 1 year ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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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.
pytorch-generative
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vq-vae-2-pytorch
Posts with mentions or reviews of vq-vae-2-pytorch.
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[D] GCP compute enging pricing question
not sure exactly what you mean by dimensionality of the forward passes, are you meaning the size of each layer? If it helps I've forked this https://github.com/rosinality/vq-vae-2-pytorch/blob/master/vqvae.py dimensionality of a single sample is [80000,3,1] ( batch size which works for me is about 8 size of the dataset is around 35000
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Using VQ-VAE to encode a matrix to a vector and back again
I am trying to use https://github.com/rosinality/vq-vae-2-pytorch for this purpose.
What are some alternatives?
When comparing pytorch-generative and vq-vae-2-pytorch you can also consider the following projects:
gansformer - Generative Adversarial Transformers
animegan2-pytorch - PyTorch implementation of AnimeGANv2
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
naver-webtoon-faces - Generative models on NAVER Webtoon faces
smaller-transformers - Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.