stylegan2 VS stylegan

Compare stylegan2 vs stylegan and see what are their differences.

stylegan2

StyleGAN2 - Official TensorFlow Implementation (by NVlabs)

stylegan

StyleGAN - Official TensorFlow Implementation (by NVlabs)
Our great sponsors
  • Scout APM - A developer's best friend. Try free for 14-days
  • Nanos - Run Linux Software Faster and Safer than Linux with Unikernels
  • SaaSHub - Software Alternatives and Reviews
stylegan2 stylegan
25 20
8,719 11,919
1.7% 1.1%
3.0 1.7
about 1 month ago 5 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.

stylegan2

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 2021-10-29.

stylegan

Posts with mentions or reviews of stylegan. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-28.
  • Greater than 99% consensus on human caused climate change in the literature
    1 project | news.ycombinator.com | 20 Oct 2021
    > Take your example about StyleGAN vs BigGan, I assume once it became clear that the latter was superior to the former that likely resulted in changes to existing architectures that then found additional improvements. This change in consensus is what enables that and is a good thing.

    Well, no. :) But I think the "no" is because of the uniqueness of ML rather than a "no" to your point in general. You might be right about other fields; I don't have experience there.

    In ML, there an enormous number of techniques. Style mixing was presented as a core feature of StyleGAN (https://arxiv.org/abs/1812.04948) and was enabled by default in the codebase (https://github.com/NVlabs/stylegan/blob/03563d18a0cf8d67d897...).

    So there's a lot of "inertia" -- for example, when StyleGAN 2 came out, style mixing was still the default (https://github.com/NVlabs/stylegan2/blob/f2f751cdc7f996e3138...).

    I haven't had time to dig into StyleGAN 3, but I suspect that style mixing might still be enabled by default.

    It wasn't until we did a detailed, methodical analysis side-by-side with BigGAN, specifically to answer the question "Why is BigGAN so much better for diverse datasets?" that, on a whim, I turned off style mixing and was astonished to see BigGAN type quality pop out of a StyleGAN type arch.

    Discoveries like that usually go unnoticed, frankly because it's a lot of effort to write a paper specifically to say "Hey, if you're training StyleGAN, definitely turn off style mixing. It only seems to work well on faces."

    However, if such a paper were to be written, and accepted into a peer-reviewed journal, then your original point would probably be valid. So I don't even know if it's worth writing all of this -- I just thought it'd be interesting to point out the "Well, not really" in this case. The knowledge ends up floating around on Twitter and Discord rather than being transmitted via scientific papers...

    But, this all does tie in to your final point:

    > Consensus is easily changed with the introduction of new data, faith hangs on no matter how much evidence is put forward that it's horseshit.

    It's remarkably easy for old, accepted ideas to hang around. You'd think it'd just be a matter of "Run the experiment; experiment proves thing; thing becomes accepted." But in practice it's felt quite different...

    The thing is, everything you're saying is true in general. As t approaches infinity, there tends to be more and more consensus about older ideas, like the existence of black holes, or the validity of laws like F=ma. So we should probably pay attention when there is 99% consensus on a particular topic.

    But, for example, one reason I wouldn't want to publish a paper claiming style mixing was bad, is because it would contradict the results of Karras, who is famous. I'd better be very certain about my claim! So there's sometimes a reluctance to contradict the consensus, too, which ends up equivalent to "faith" in your example -- we have faith that famous scientists are correct. (They usually are.)

    As a cherry on top, I'll just leave a link to Feynman's messenger lectures: https://www.youtube.com/watch?v=-kFOXP026eE&ab_channel=TalkR... ... the history of science is fascinating. I'd dreamt for years of becoming a scientist, but the actual experience turned out to be surprisingly different than what I thought it'd be. I love it though -- all these weird corner cases are the spice of life.

  • Fakemon generated by AI
    1 project | reddit.com/r/fakemon | 28 Sep 2021
  • [1812.04948] A Style-Based Generator Architecture for Generative Adversarial Networks
    3 projects | reddit.com/r/LatestInML | 28 Aug 2021
    2 projects | reddit.com/r/Regressions | 3 Jun 2021
    PDF link Landing page
    2 projects | reddit.com/r/Regressions | 3 Jun 2021
  • Innovative Technology NVIDIA StyleGAN2
    5 projects | dev.to | 15 Jun 2021
    Code: https://github.com/NVlabs/stylegan
    5 projects | dev.to | 15 Jun 2021
    https://arxiv.org/abs/1812.04948 (A Style-Based Generator Architecture for Generative Adversarial Networks)
  • AI made this stormy, overgrown, flodded version of the leaked image, I thought I'd share.
    1 project | reddit.com/r/Battlefield6 | 3 May 2021
    if anyone wonders I used Style2Gan NVlabs/stylegan: StyleGAN - Official TensorFlow Implementation (github.com)
  • Technoalchemy - a short spiritual book combining GPT-3 text and art made with StyleGAN + other ML techniques
    3 projects | reddit.com/r/MediaSynthesis | 18 Apr 2021
    Check out the PDF here I've been calling this "the first spiritual guidebook written by an AI". The text was written almost entirely with GPT-3, minus the couple of paragraphs I wrote as prompt. The art was made with various tools - for instance, the cover was made in part with Aphantasia (CLIP+FFT), the title page is an old public domain photo colorized with DeOldify, the faces made with StyleGAN, both standalone and via Artbreeder. Let me know what you think - I have a couple bigger projects building off of the techniques I used, and I would appreciate any feedback!
  • egg🖥️irl
    1 project | reddit.com/r/egg_irl | 2 Apr 2021
    (Every person here is fake - the faces on the ends and the transition between them were created using StyleGAN)

What are some alternatives?

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

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

lucid-sonic-dreams

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

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

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

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

aphantasia - CLIP + FFT/DWT/RGB = text to image/video

LiminalGan - A stylegan2 model trained on liminal space images

waifu2x - Image Super-Resolution for Anime-Style Art