stylegan2-pytorch VS stylegan2-ada

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/

If you smudge just a few locations I doubt it would fool a simple discriminator. You could also train a discriminator that is robust to post-processing by using augmentations. This was popular with StyleGAN models: https://github.com/NVlabs/stylegan2-ada

https://github.com/NVlabs/stylegan2-ada + clip guided diffusion

StyleGAN2-ADA is a neural network good at learning styles from images, you can give it a dataset and 'learn' its style into a file (a trained model). In this example, I load a model and given a random seed, generate a random texture which is applied to the object's material.

You have to modify the code, it's line 60 in https://github.com/NVlabs/stylegan2-ada/blob/main/generate.py

no i dont.. but as for training - i just use default tf stylegan2-ada repo ( https://github.com/NVlabs/stylegan2-ada )

You should check stylegan-2 ada, it works on colab and can be trained less than 12 hours tensorflow implementation