dalle-2-preview VS stylegan2-pytorch

We believe that many decisions about our defaults and hard bounds should be made collectively, and while practical implementation is a challenge, we aim to include as many perspectives as possible. As a starting point, we’ve sought external input on our technology in the form of red teaming. We also recently began soliciting public input on AI in education (one particularly important context in which our technology is being deployed).

From this OpenAI document:

It's also of note that you can't sell a game using this method, as Dalle-2's terms of service prevent use in commercial projects. It's hard to justify rate of return considering you can only ever give it away for free, and even in that case there are some uncertain legal elements regarding copyright and the images that are used to train the dataset.

On their GitHub https://github.com/openai/dalle-2-preview/blob/main/system-card.md I can only see references to v1.

"b) Exploration of the bolded part of OpenAI's comment "Each generated image includes a signature in the lower right corner, with the goal of indicating when DALL·E 2 helped generate a certain image." (source)." (source link: https://github.com/openai/dalle-2-preview/blob/main/system-c...)

I feel the DALL-E 2 watermark signature could be a seed or something.

DALL-E's docs for example mention it can output whole copyrighted logos and characters[1] and understands it's possible to generate human faces that are bear the likeness of those in the training data. We've also seen people recently critique Stable Diffusion's output for attempting to recreate artists' signatures that came from the commercial trained data.

That said by a certain point the kinks will be ironed out and likely skirt around such issues by only incorporating/manipulating just enough to be considered fair use and creative transformation.

[1] "The model can generate known entities including trademarked logos and copyrighted characters." https://github.com/openai/dalle-2-preview/blob/main/system-c...

Some types of content were purposely excluded from the training dataset(s) (source).

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/