alias-free-gan
NVAE
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alias-free-gan | NVAE | |
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3 | 3 | |
1,317 | 958 | |
-0.2% | 2.9% | |
1.8 | 0.0 | |
over 2 years ago | over 1 year ago | |
Python | ||
- | GNU General Public License v3.0 or later |
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alias-free-gan
- When is the alias-free GAN code going to be released?
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Anime Alias-Free GAN Interpolation
Curious how you managed to make this since the code hasn't been released yet https://github.com/NVlabs/alias-free-gan Did you write it from the research paper, if so do you have a github link?
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Alias-Free GAN
This isn't true. I do ML every day. You are mistaken.
I click the website. I search "model". I see two results. Oh no, that means no download link to model.
I go to the github. Maybe model download link is there. I see zero code: https://github.com/NVlabs/alias-free-gan
zero code. Zero model.
You, and everyone like you, who are gushing with praise and hypnotized by pretty images and a nice-looking pdf, are doing damage by saying that this is correct and normal.
The thing that's useful to me, first and foremost, is a model. Code alone isn't useful.
Code, however, is the recipe to create the model. It might take 400 hours on a V100, and it might not actually result in the model being created, but it slightly helps me.
There is no code here.
Do you think that the pdf is helpful? Yeah, maybe. But I'm starting to suspect that the pdf is in fact a tech demo for nVidia, not a scientific contribution whose purpose is to be helpful to people like me.
Okay? Model first. Code second. Paper third.
Every time a tech demo like this comes out, I'd like you to check that those things exist, in that order. If it doesn't, it's not reproducible science. It's a tech demo.
I need to write something about this somewhere, because a large number of people seem to be caught in this spell. You're definitely not alone, and I'm sorry for sounding like I was singling you out. I just loaded up the comment section, saw your comment, thought "Oh, awesome!" clicked through, and went "Oh no..."
NVAE
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[R] Looking for papers which are modified variational autoencoder (VAE)
NVAE: A Deep Hierarchical Variational Autoencoder
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Alias-Free GAN
non-commercially.
It's a great example of the difference between open source (which it is) and free software which it is not. So we're back to square one where it is probably best to clean-room the implementation from the paper, which is nearly useless to reproduce the model.
[1] https://github.com/NVlabs/NVAE/blob/master/LICENSE
What are some alternatives?
StyleCLIP - Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
compare_gan - Compare GAN code.
alias-free-gan-pytorch - Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch
alias-free-gan - Unofficial Alias-Free GAN implementation. Based on rosinality's version with expanded training and inference options.