vision-aided-gan VS StyleSwin

Compare vision-aided-gan vs StyleSwin and see what are their differences.

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vision-aided-gan StyleSwin
3 1
356 395
- 3.0%
2.2 0.0
9 months ago 2 months ago
Python Python
MIT License 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.


Posts with mentions or reviews of vision-aided-gan. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-16.


Posts with mentions or reviews of StyleSwin. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning StyleSwin yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing vision-aided-gan and StyleSwin you can also consider the following projects:

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

Anime2Sketch - A sketch extractor for anime/illustration.

ArtGAN - ArtGAN + WikiArt: This work presents a series of new approaches to improve GAN for conditional image synthesis and we name the proposed model as “ArtGAN”.

contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

APDrawingGAN - Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)