Hello everyone, while a couple of my experiments are blocked for various reasons, I decided to look into Laion’s image datasets and explore what’s inside.
It’s important to understand the different parts that make Stable Diffusion (or other AI models) and images used to train the model are at the very core of it. Understanding what went inside should allow us to optimize different workflows.
Sharpening technical skills to get used to such databases for various future tasks. Whether it is for fine-tuning or other possible use cases.
There is more to it than just learning about what’s inside the dataset. There are very practical things that these image datasets can be used for:
Regularization image sets: for Dreambooth fine-tunes in particular, one can scrape these datasets for specific class images and use them for regularization purposes. We haven’t experimented much on this vs. using SD-generated images, but these are a good chance to achieve interesting results.
Fine-tuning on specific subsets: one can scrape images for a specific subject or object, select a high-quality subset, potentially re-caption them and then fine-tune the model with this dataset. Higher quality output is almost guaranteed