dataset-tools VS stylegan2-ada-pytorch

I made it automatically with a dataset tool. Here is the link: https://github.com/dvschultz/dataset-tools

I am very glad that my work aroused interest. Thank you all! I will try to answer the questions. How did i create this? I collected a dataset of about 600 images of dark art sketches, processed them so that they would be suitable for training in the StyleGAN2-ada (resized to 1024x1024, edited something a little, made sure that all images have 3 channels). Mainly used photoshop, also Duplicate Photos Fixer Pro to find duplicates, and also I highly recommend Derek's dataset-tools for preparing datasets. Then the dataset was archived, uploaded to GoogleDrive and added to Google Colab for training. I had to subscribe to the Colab Pro because the free version could not start training due to lack of memory. A pro subscription costs $10 per month and provides advantages in capacity and uptime. More about working with Google Colab can be found here. I'm a beginner myself, so no secret techniques have been applied. In fact, everything is as in the video tutorial. Training took place about 30 hours, partly at Tesla P100 and partly on a more powerful Tesla V100. I have not written anywhere an article about this project because I do not speak English very well. And there is not much to write about, everything is simple. In the future I will probably post the .pkl file to the public.

You can use this GitHub repository of StyleGAN2-ada and audio reactive visualize it with this GitHub repository of lucidsonicdreams

StyleGAN2-ADA [Playlist] How to create a dataset? [Playlist] How to train StyleGAN? [Video] This is a notebook I used (first Colab in the list) [Code] What is Google Colab? [Video]

I used this notebook (the first one): https://github.com/dvschultz/stylegan2-ada-pytorch

I use this repo. In my experience the PyTorch implementation is way better and faster

Not sure what that is but no, used StyleGAN2-ADA to train on my own dataset of images of myself and then generated the interpolation with the resulting network. Most of the code used was from dvschultz's Colab notebooks.