For the same reason they cannot do hands very well. There just aren’t enough bits in their internal representations to encode specific details of that type. Scaling up the model can help, but that requires something on the order of 100× as much computer time and storage. Since the model you are using may already have taken months and dozens or hundreds of millions of dollars to create, the company behind it may be reluctant to spend years and billions to replace it.
Given the amount of training image data with hands (and text in the image), I don’t understand the lack of specific detail. For hands it’s even stranger - the models add fingers, for example, which seems like something that the training data never sees.
I don't think it's nearly as simple as there not being enough bits. I think the structure of the model just isn't designed to effectively encode that sort of information. The average real finger is between other fingers, which is probably why generated hands sometimes end up with too many.
Comments
For the same reason they cannot do hands very well. There just aren’t enough bits in their internal representations to encode specific details of that type. Scaling up the model can help, but that requires something on the order of 100× as much computer time and storage. Since the model you are using may already have taken months and dozens or hundreds of millions of dollars to create, the company behind it may be reluctant to spend years and billions to replace it.
Given the amount of training image data with hands (and text in the image), I don’t understand the lack of specific detail. For hands it’s even stranger - the models add fingers, for example, which seems like something that the training data never sees.
I don't think it's nearly as simple as there not being enough bits. I think the structure of the model just isn't designed to effectively encode that sort of information. The average real finger is between other fingers, which is probably why generated hands sometimes end up with too many.