I believe lighting plays a very important part in making a scene realistic when it comes to creating one artificially, like in 3D modelling. That is why I also think the lighting of these AI generated images is the prime source of what impresses people about these images since no matter how unrealistic or distorted the subject is, the lighting makes it look like a natural part of the background. This is clearly different from photos like from poorly Photoshopped ones where the subject feels deliberately inserted into the scene from a cutout.
I am interested to understand how LLMs understand the context of the lighting when creating images. Do they make use of samples which happen to have the exact same lighting positions or do they add the lighting as an overlay instead? Also, why is it that lighting doesn’t look convincing in some cases like having multiple subjects together etc.?
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.