alias-free-gan VS StyleCLIP

Compare alias-free-gan vs StyleCLIP and see what are their differences.

alias-free-gan

Alias-Free GAN project website and code (by NVlabs)

StyleCLIP

Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral) (by orpatashnik)
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alias-free-gan StyleCLIP
3 23
1,320 3,863
0.0% -
1.8 0.0
over 2 years ago 10 months ago
HTML
- 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.
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.

alias-free-gan

Posts with mentions or reviews of alias-free-gan. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-30.
  • When is the alias-free GAN code going to be released?
    3 projects | /r/nvidia | 30 Sep 2021
  • Anime Alias-Free GAN Interpolation
    2 projects | /r/artificial | 20 Aug 2021
    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?
  • Alias-Free GAN
    5 projects | news.ycombinator.com | 23 Jun 2021
    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..."

StyleCLIP

Posts with mentions or reviews of StyleCLIP. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-13.

What are some alternatives?

When comparing alias-free-gan and StyleCLIP you can also consider the following projects:

encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766

compare_gan - Compare GAN code.

NVAE - The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)

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

pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework

tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Story2Hallucination

CLIP-Style-Transfer - Doing style transfer with linguistic features using OpenAI's CLIP.

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

stylegan-xl - [SIGGRAPH'22] StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets