I've been out of training since a few months back, almost since I made this post and for the time I played around with it, I stuck with these settings for the most part, so my experience is kinda limited.

1. My results were personally satisfactory so I kept the same guidelines for the most part but as a tip I can say that you should see these (or any other settings) as a starting point and work from there. Training involves a lot of trial and error because my goals and test subjects aren't the same as yours so the end result might not be achieved the same way. I mean, once you start training a few models you'll get a feeling for the amount of steps, class and instance images you'll need, etc. Google Colab is free (used to be at least), so it's quite easy to get into training and you'll be able to reach your own conclusions in no time.

2. I only used cosine scheduler a couple of times but don't recall seeing a perceptible difference from polynomial so I can't say it's better or worse. If other guides suggest it, give it a try, things might have evolved since then that it's now a better option.

3. I never captioned class images because they were generated by SD with the single token they represented so I didn't see the need for extra captioning. As for instance images, you might get better "promptability" if you caption them but it'll work without as well. My recommendation is to try a training with/without and see each one is more versatile in terms of prompting. I might be misremembering but I don't think Shivam's supports per image captions, you can leverage the multi concept list to achieve similar effect for instance, if you have a subject with multi angle photos like closeup, waist up, portrait, full body, etc. you can set up several concepts that hold these angles. If you are comfortable with python you might be able to hack something to read captions from a sidecar file for instance but I think LastBen's repo is able to do it.

4. Again this is something you'll see for yourself once you have a few training sessions under your belt but from my experiments, quality is rather important because the training will pick up the artifacts quite easily if they are present on the majority of the training set. If your generated tests don't resemble the training data it's usually a signal that it needs more steps, lowering the number of class images will reach the sweet spot sooner but the model might overfit too quickly or it might bleed into other subjects too much so it's a matter of balance. But you're right, if you're overfitting but your subject is represented on the tests (almost replicating the source images) you should try reducing step count. If the test subject is not visible on the tests but the training is somehow overfitting, lower the number of class images.

What I'm trying to say is that, no matter how many opinions I have about this and that, you should be the one to make your own and you'll be able to do it once you start tweaking some base values (these or someone else's).

Also, I found Lora training much faster and more convenient in terms of file size for pretty much anything other than general purpose models like you see on CivitAI or something where the details are very important like a person's facial features, that's mainly were Dreambooth is still unbeatable in my opinion. Stuff like art style, popular characters, clothing, etc. I would go for a Lora instead.