awesome-pretrained-stylegan2 VS stylegan

Many of the items on the first list below are Google Colaboratory ("Colab") notebooks, which run in a web browser; for more info, see the Google Colab FAQ. Some Colab notebooks create output files in the remote computer's file system; these files can be accessed by clicking the Files icon in the left part of the Colab window. For the BigGAN image generators on the first list that allow the initial class (i.e. type of object) to be specified, here is a list of the 1,000 BigGAN classes. For the StyleGAN image generators on the first list that allow the specification of the StyleGAN2 .pkl file, here is a list of them.

There are so many ways to work with AI art, the best way if you don't know any code or AI library is to use a collab paper, i used sonic lucid dream which is a video generator based on Stylegan 2 in which i used a pretrained stylegan model called Trypohobia and a song from some wierd album i digged, a full list of pretrained models can be found here, i setup my own from their github repo, but you can use this collab paper to try it yourself using google's GPUs, minimal knowledge about python and GAN theory, that's an hour read max.Have fun !

Hi! That's the point, you actually don't need an entire dataset. Just a pretrained generator and a few sketches of the poses that you want to generate! For example, you can take any model from https://github.com/justinpinkney/awesome-pretrained-stylegan2 sketch a couple of target images and apply the "Sketch Your Own GAN" method. If you have any more questions, I'll try to answer them.

For some more good pretrained StyleGAN2 weights: https://github.com/justinpinkney/awesome-pretrained-stylegan2 (unfortunately some of the download links are dead though)

I suspect it was WikiArt from justinpinkey's StyleGAN2 collection.

I found this page after a quick google search https://github.com/justinpinkney/awesome-pretrained-stylegan2 but if this one doesn't work there are others. You can also just use StyleGan (v1) and have great results, I'm not sure that the v2 is much better.

like these models: https://github.com/justinpinkney/awesome-pretrained-stylegan2

Been following generative AI since 2017 when nvidia released their first GAN paper & the results always fascinated me. Trained my own models with their repo then experimented with other open source projects. went thru the pain of assembling my own data set, tweaking code parameters to achieve what i'm looking for, had to deal with all kinds of hardware/software issues. I know it's not easy. (screenshot of a motorbike GAN model i was training in 2018 https://imgur.com/a/SIULFhR, was taken after 5 hours of training on a gtx 1080) or this, cinema camera output from another locally trained model. So yeah i have a couple ideas of how generative AI works. yup things were that bad few years ago, that technology has come a long way. Using & setting up something like stable diffusion with automatic1111 webui isn't really a complex process. Though generating AI art locally is always gonna feel more rewarding than using a cloud based service.

Their algorithm is public, you could do it yourself if you have the proper hardware: https://github.com/NVlabs/stylegan

Umm, StyleGAN was the first decent image generation model, and it was producing great images from random seeds 5 years ago. Now, that's with the obvious caveat that each model was trained to produce one specific type of image and it helped immensely if the training images were all aligned the same. Diffusion models are certainly the trendy current architecture for image generation, but AFAIK there's no fundamental theoretical limitation to the output quality of any architecture except the general rule that more parameters is better.

Jokes aside, ML moves extremely fast and our field is quickly advancing. The honest truth is that no researcher can even keep up other than their extremely niche corner. I'll show you an example. Here's what state of the art image generation looked like in 2014, 2018, and here is today (which now is highly controllable using text prompts instead of data prompts).

The fundamental technology we will use in this work is a generative adversarial network. Specifically, the Style GAN variant.

Based on my findings, we don't really need FP64 unless it's for certain medical applications. But The Best GPUs for Deep Learning in 2020 — An In-depth Analysis is suggesting A100 outperforms A6000 ~50% in DL. Also the Stylegan project  GitHub - NVlabs/stylegan: StyleGAN - Official TensorFlow Implementation uses NVIDIA DGX-1 with 8 Tesla V100 16G(Fp32=15TFLOPS) to train dataset of  high-res 1024*1024 images, I'm getting a bit uncertain if my specific tasks would require FP64 since my dataset is also high-res images. If not, can I assume A6000*5(total 120G) could provide similar results for StyleGan?

Yes that's what I thought. But StyleGan https://github.com/NVlabs/stylegan uses NVIDIA DGX-1 with 8 Tesla V100 16G GPUs(FP32=15) to do the training, not sure if it's related to its high-res training images or something else.