No Stupid Questions
No such thing. Ask away!
!nostupidquestions is a community dedicated to being helpful and answering each others' questions on various topics.
The rules for posting and commenting, besides the rules defined here for lemmy.world, are as follows:
Rules (interactive)
Rule 1- All posts must be legitimate questions. All post titles must include a question.
All posts must be legitimate questions, and all post titles must include a question. Questions that are joke or trolling questions, memes, song lyrics as title, etc. are not allowed here. See Rule 6 for all exceptions.
Rule 2- Your question subject cannot be illegal or NSFW material.
Your question subject cannot be illegal or NSFW material. You will be warned first, banned second.
Rule 3- Do not seek mental, medical and professional help here.
Do not seek mental, medical and professional help here. Breaking this rule will not get you or your post removed, but it will put you at risk, and possibly in danger.
Rule 4- No self promotion or upvote-farming of any kind.
That's it.
Rule 5- No baiting or sealioning or promoting an agenda.
Questions which, instead of being of an innocuous nature, are specifically intended (based on reports and in the opinion of our crack moderation team) to bait users into ideological wars on charged political topics will be removed and the authors warned - or banned - depending on severity.
Rule 6- Regarding META posts and joke questions.
Provided it is about the community itself, you may post non-question posts using the [META] tag on your post title.
On fridays, you are allowed to post meme and troll questions, on the condition that it's in text format only, and conforms with our other rules. These posts MUST include the [NSQ Friday] tag in their title.
If you post a serious question on friday and are looking only for legitimate answers, then please include the [Serious] tag on your post. Irrelevant replies will then be removed by moderators.
Rule 7- You can't intentionally annoy, mock, or harass other members.
If you intentionally annoy, mock, harass, or discriminate against any individual member, you will be removed.
Likewise, if you are a member, sympathiser or a resemblant of a movement that is known to largely hate, mock, discriminate against, and/or want to take lives of a group of people, and you were provably vocal about your hate, then you will be banned on sight.
Rule 8- All comments should try to stay relevant to their parent content.
Rule 9- Reposts from other platforms are not allowed.
Let everyone have their own content.
Rule 10- Majority of bots aren't allowed to participate here.
Credits
Our breathtaking icon was bestowed upon us by @Cevilia!
The greatest banner of all time: by @TheOneWithTheHair!
view the rest of the comments
It certainly doesn't always get it right - I've seen subjects lit by bright sunlight in a nighttime background, or just from a wildly different direction, but within a subject the lighting usually seems consistent.
I've wondered the same thing myself, my assumption is that it just correlates how lighting works across millions of training images, much like how it manages to get gravity right most of the time.
I'm an AI researcher and yes, that's basically right. There is no special "lighting mechanism" portion of the network designed before training. Just, after seeing enough images with correct lighting (either for text to image transformer models or GANs), it will understand what correct lighting should look like. It's all about the distribution of the training data. A simple example is this-person-does-not-exist.com. All of the training images are high resolution, close-up, well-lit headshots. If all the training data instead had unrealistic lighting, you would get unrealistic lighting out. If it's something like 50/50, you'll get every part of that spectrum between good lighting and bad lighting at the output.
That's not to say that the overall training scheme of especially something like GPT-4 doesn't include secondary training operations for more complex tasks. But lighting of images is a simple thing to get correct with enough training images.
As an aside, I said that website above is a simple example, but I remember less than 6 years ago when that came out and it was revolutionary, so it's crazy how fast the space has moved forward in such a short time.
Edit: to answer the multiple subjects question: it probably has seen fewer images with multiple subjects and doesn't have enough "knowledge" from it's training data to accurately apply lighting in those scenarios. And you can imagine lighting is more complex in a scene with more subjects so it's more difficult for the model to use a general solution it's seen many times to fit the more complex problem.