casual VS stylegan2-pytorch

Compare casual vs stylegan2-pytorch and see what are their differences.

stylegan2-pytorch

Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement (by lucidrains)
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casual stylegan2-pytorch
4 1,989
3,024 3,613
- -
0.0 0.0
10 months ago over 1 year ago
JavaScript Python
- 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.

casual

Posts with mentions or reviews of casual. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-30.
  • Show HN: UI Filler – placeholders for your designs
    5 projects | news.ycombinator.com | 30 Jun 2022
    Faker looks cool! We've been using Casual [0] for ages but Faker looks more featureful and more actively maintained.

    0: https://github.com/boo1ean/casual

  • How to Mock an API with random data from NodeJS
    1 project | dev.to | 3 Apr 2022
    In order to create the mockserver we need 2 npm dependencies. json-server and casual so we run npm install json-server casual --save-dev in our project.
  • 📦 Top alternatives for "colors" and "faker"
    6 projects | dev.to | 10 Jan 2022
    https://npm.im/casual (57k weekly downloads) https://github.com/boo1ean/casual (2.8k stars)
  • Generating Fake Data for Dashboards and Data Visualizations
    2 projects | dev.to | 8 Jan 2021
    In the past, I have relied on Google Sheets and a combination of array formulas, vlookups, random value generators to create fake datasets. This approach worked for small, one off datasets. Any time I needed to generate more than 1000 records or needed to update a dataset, this approach proved to be tedious. I also explored Faker and Casual (both of which are fantastic tools) but found that the data they generate was too abstract and that the implementation wasn't ideal for large datasets. I wanted a tool that:

stylegan2-pytorch

Posts with mentions or reviews of stylegan2-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-06.

What are some alternatives?

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

Faker.js - What really happened with Aaron Swartz?

DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.

typeorm-seeding - 🌱 A delightful way to seed test data into your database.

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

chance.js - Chance - Random generator helper for JavaScript

VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

faker - Generate massive amounts of fake data in the browser and node.js

stylegan2-ada - StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

plop - Consistency Made Simple

mediapipe - Cross-platform, customizable ML solutions for live and streaming media.

data-spring - Easily generate a dummy dataset based on a provided config

dalle-mini - DALL·E Mini - Generate images from a text prompt