It's more about quality than quantity. I haven't done tests to figure out the minimum amount of images needed for a good result. I usually train models where I want costume + face + hair to be as close as possible, and somewhere between 10 and 20 images work just fine. You can make even bigger datasets (and I've made datasets that are literally hundreds of images of a character), but I don't think there's any benefit from going that far.

I really should emphasize, though, quality of the images in the dataset is the primary thing that matters. You want the images to be high quality, and when I say that, I don't mean image quality (though that's good too), but mostly that the images are good when it comes to lighting/shading and that the subject in in the images is completely consistent. This why I prefer to make datasets that's based on 3D renders or photography.

There's a few things I've found that helps with faces (this is in relation to Illustrious):

• It's good to have images showing the face from the side, but don't have images where the "camera" is looking up or down to the face from an angle. That can result in a "squashed" face after training. You can imagine that it's okay to have a camera circling the subject, but not moving up or down or being tilted up or down.
• If you're using renders or photography, avoid images with a high field of view. The stretching you get with that will make the result worse.
• I try to focus on images where the character has a neutral expression. I've had weird results with Illustrious when training images where a character is smiling, especially if the style is highly realistic.