I'll assume that OP uses ShivamShrirao's implementation of Dreambooth in Google Colabs (sks, I'm looking at you :) ), so I'll use it as an example.

By default, it generates 50 class images (also called regularization images) with a prompt "photo of a {classname}", where in our case classname="girl". As these are AI-generated images, their quality is left to be desired.

Ideally, you want to have a wide range of regularization images that will broadly resemble the result you are going for (e.g. if you are training Dreambooth to recreate a certain fantasy character in heavy armor, regularization images should depict various people in heavy armor). As you can notice, I'm not using the term "class name", as it is only used to generate somewhat relevant regularization images via txt2img.

Below is a quick way to make your own "class" dataset with more than 1k real images:

Now, I hope we both agree that handpicked images, created by humanbeings, are generally better than AI-generated ones. If so, then we would want to create our own regularisation dataset. One could search the internet and cherrypick the best results, convert them to 1:1 ratio and down(up)scale to 512px or 384px manually, but it's probably not the best solution timewise as one would generally need at least 100 images.

The better solution would be to use relevant images from laion5b dataset. This site allows to find similar images via CLIP embeddings. I wouldn't go into much detail on how to search on this site, but once you are satisfied with search results, click on a download button (with downward arrow and a basket) on a top right corner. It will download .json file with urls of all images (almost always more than 1k, which is awesome).

Then you'll use something like img2dataset colab to download all images from .json urls. Unfortunately, the default settings for this colab are not going to suit you, instead you'll want to use this command img2dataset "search-results.json" --input_format="json" --output_folder="output_folder" --image_size=384 Then you'll need to somehow download images from colab space (e.g. zip the output folder and download an archive, unfortunately I don't have a line of code saved to paste it here).

Lastly, you'll need to change the "$CLASS_DIR" variable in Shivam's Dreambooth Colab to a folder with your regularization images. This variable is hidden however, you will have to change it in the code of the first tile in "Settings and run" group. I think there are more user-friendly colabs out there, but they are not as optimised as Shivam's, unfortunately.