Did-Somebody-Say-Corgi
Image Synthesis + Corgis = <3 (by seawee1)
stylegan2-fun
Slight modifications to the official StyleGAN2 implementation (by PDillis)
| Did-Somebody-Say-Corgi | stylegan2-fun | |
|---|---|---|
| 3 | 1 | |
| 87 | 49 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | about 3 years ago | |
| Jupyter Notebook | Python | |
| - | GNU General Public License v3.0 or later |
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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.
Did-Somebody-Say-Corgi
Posts with mentions or reviews of Did-Somebody-Say-Corgi.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-13.
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AI-based corgi synthesis (x-post r/machinelearning)
Have a look at the GitHub page for more information. You can use this Colab Notebook if you'd like synthesize your own corgi images or videos! You probably need some basic Python knowledge though.
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[P] StyleGAN2-ADA trained on cute corgi images <3
Have a look at my [GitHub page](https://github.com/seawee1/Did-Somebody-Say-Corgi) for more information. You'll also find all the links there, i.e. one to the dataset (eventhough I'm not sure if anybody would actually need such a dataset :D) and the model checkpoints.
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StyleGAN2 model trained on images of corgis
It took quite some time to aquire and preprocess the high-resolution corgi dataset, and even more time to train the model using Colab Pro (~18 days). Have a look at my [https://github.com/seawee1/Did-Somebody-Say-Corgi](GitHub page) for more information. You can also play around with it using this Colab notebook.
stylegan2-fun
Posts with mentions or reviews of stylegan2-fun.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-13.
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[P] StyleGAN2-ADA trained on cute corgi images <3
Sure! https://github.com/PDillis/stylegan2-fun#random-interpolation The code is doing a random interpolation, so if you want to go between specific seeds you can see further below. I'm currently porting everything to the ADA Pytorch version, and my current tests note it's far more efficient memory-wise. In the meantime, you can use that one for the StyleGAN1 and 2 models, though the ADA ones will need a bit of modification.
What are some alternatives?
When comparing Did-Somebody-Say-Corgi and stylegan2-fun you can also consider the following projects:
models - A collection of pre-trained, state-of-the-art models in the ONNX format
instagram-scraper - Instagram Scraper. Scrape useful data/posts from instagram users, hashtag and locations pages. Comments and people who liked specific posts and soon more. No login or API keys are required
DualStyleGAN - [CVPR 2022] Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
stylegan2-ada - StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation
SatelliteCloudGenerator - A PyTorch-based tool to generate clouds for satellite images.