stylegan2-pytorch VS awesome-pretrained-stylegan2

export default function ChannelListBottomBar(): JSX.Element { const { client } = useChatContext(); const [micActive, setMicActive] = useState(false); const [audioActive, setAudioActive] = useState(false); return (

{client.user?.image && (

div> )}

{client.user?.name} span> {client.user?.online ? 'Online' : 'Offline'} span> p> button>

setMicActive((currentValue) => !currentValue)} > button> setAudioActive((currentValue) => !currentValue)} > button> button> div> ); }

I wonder if we will ever get a realism model which can produce normal faces like https://thispersondoesnotexist.com instead of like super symmetrical faces of models

this has been in circulation since a while AI images took off, and they certainly weren't convincing before they did. You know the old "try to name one thing in this image" macro? Pretty sure that was AI generated, there was also thispersondoesnotexist.com which was always pretty good but of course it is

Not the workflow for these images, but if you easily want to spice up your gens with a bit more natural look, try using images from thispersondoesnotexist.com with IPAdapter face model.

...then they just use the app on generated images of not real people (potentially based on specific inputs to remind you of a real person).

Also we can generate very realistic faces with AI, https://thispersondoesnotexist.com/ (old example), fully 3D faces are doable at this point. So in another 5-10 years a low profile model like him wouldn't even be hired for this, they would just generate a digital face model. Unionizing will only speed up studios adoption of digital replacements.

It looks like they all use a profile picture made with https://thispersondoesnotexist.com/

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