stylegan2-ada VS stylegan2

Compare stylegan2-ada vs stylegan2 and see what are their differences.


StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation (by NVlabs)


StyleGAN2 - Official TensorFlow Implementation (by NVlabs)
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stylegan2-ada stylegan2
18 20
1,421 8,516
4.4% 2.8%
1.3 1.5
9 months ago 4 days ago
Python Python
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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.
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.


Posts with mentions or reviews of stylegan2-ada. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-14.
  • AI will shape up the workflow of the future. Here's a simple implementation of NVidia's StyleGAN inside Blender! | 2021-09-13
    StyleGAN2-ADA is a neural network good at learning styles from images, you can give it a dataset and 'learn' its style into a file (a trained model). In this example, I load a model and given a random seed, generate a random texture which is applied to the object's material.
  • How do you generate those latent walk animations?
    I understand the concept you're describing, but I'm unsure how to implement it in practice. The documentation on Stylegan2's GitHub page doesn't mention anything on it I can see.
    You have to modify the code, it's line 60 in
  • [D] Do I need to apply spectral norm to my embedding matrix when training a conditional W-GAN?
  • Can I train a model on 100 images of homes and have it draw a couple "average" homes?
  • New 'The Sculpture 3'. 3d sculpting + neural network | 2021-04-27
    no i dont.. but as for training - i just use default tf stylegan2-ada repo ( )
  • [R] EigenGAN: Layer-Wise Eigen-Learning for GANs
    You should check stylegan-2 ada, it works on colab and can be trained less than 12 hours tensorflow implementation
  • gamma
  • I have been making art with deep neural networks lately, and at some point one of them started outputting these cosmic creatures, so I thought I'd share. | 2021-04-21
    This image looks awesome! And this process sounds really interesting, any more specific tips on where to get started? I studied Neuroscience, use Linux and Python for data analysis, but haven't done any image editing yet. I found the code but it seems like I need a GPU. Which I don't have, unless I use the servers at my university lol
  • An interpolation from an AI trained on liminal images
    with a process called ADA


Posts with mentions or reviews of stylegan2. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-14.
  • AI generated image for "Kerbal Space Program".
    If you’d like to know more, here’s a video explaining the method and the paper:
  • Pre-trained StyleGAN2 model
    The implementation and trained models are available on the StyleGAN2 GitHub repo.
  • AI-generated art day 3: Princess Luna!
    If you haven’t seen the previous posts, this art was made possible by This Pony Does Not Exist, which is powered by Nvidia’s StyleGAN2 AI. You can read more about the AI here.
  • [D] Paper regarding all ML problems reducing to diff eq..?
    Neural SDEs are the continuous-time limit of recurrent networks with noise as input. For example, StyleGAN2 is of this form. You can train Neural SDEs as VAEs or as GANs.
  • [R] Alias-Free GAN
  • Innovative Technology NVIDIA StyleGAN2 | 2021-06-15
  • GitHub - NVlabs/stylegan2: StyleGAN2 - Official TensorFlow Implementation
  • StyleGanV2 Nebula Loop! | 2021-05-31
    Oversimplified, a machine learning model learns how space photos should look like and by varying the initial conditions we can create each frame of the clip. This part can be fairly extensive and if you would like to learn more I recommend checking out CGP Grey's video, 3Blue1Brown's series and StyleGanV2s github page itself
  • StyleGAN2 implementation in PyTorch with side-by-side notes | 2021-05-23
    Paper on arXiv
  • Final Year Project on DCGAN for MNIST: Need Advice from the Wise Minds of Reddit
    Most improvements from recent GANs' papers usually tinker with the network architecture or loss functions used. Eg. recent near-human quality StyleGANv2 used progressive growing networks, or the GAN that coined image-to-image translation used modified loss function to better learn mapping between images. You can try explore those two factors

What are some alternatives?

When comparing stylegan2-ada and stylegan2 you can also consider the following projects:

awesome-pretrained-stylegan2 - A collection of pre-trained StyleGAN 2 models to download

stylegan - StyleGAN - Official TensorFlow Implementation

stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

stylegan2_pytorch - A Pytorch implementation of StyleGAN2


pix2pix - Image-to-image translation with conditional adversarial nets

ffhq-dataset - Flickr-Faces-HQ Dataset (FFHQ)