Do you happen to have any screenshots of what you mean? I’m really curious to see dreambooth’s capabilities in the field, and it sounds like you’ve had experience with some of its pitfalls.
Basically OP says that overfitting is a common pitfall, you don't want to overtrain the model, because then everything will look like your training data, and vice versa with not enough training steps. So it's a bit of a balance. If you search for "dreambooth" on the SD subreddit, you will see a lot of examples of dreambooth results and also some that show overfitted and underfitted results. https://www.reddit.com/r/StableDiffusion/search?q=dreambooth...
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Do you happen to have any screenshots of what you mean? I’m really curious to see dreambooth’s capabilities in the field, and it sounds like you’ve had experience with some of its pitfalls.
Basically OP says that overfitting is a common pitfall, you don't want to overtrain the model, because then everything will look like your training data, and vice versa with not enough training steps. So it's a bit of a balance. If you search for "dreambooth" on the SD subreddit, you will see a lot of examples of dreambooth results and also some that show overfitted and underfitted results. https://www.reddit.com/r/StableDiffusion/search?q=dreambooth...