Slider LoRa for Controlling Social and Cultural Status based on cultural capital

Slider LoRa allows users to finely control the social status based on the cultural capital / education level of an individual. This model is designed to explore how machine learning models can represent different social status categories.

DISCLAIMER: As a sociology student, I was interested in experimenting with how models can represent social and cultural capital. Please note that social status and cultural capital are a complex phenomena that cannot be reduced to one's clothing or general outlook. Additionally, I am aware that the model may have racial bias, which was not intentional.

What does it do?

Lets the user change the socio-cultural status of the individual.

How to use it?

Add to prompt just like any other LoRa, but adjust the weight from negative -5 to +5 to achieve roughly the following results:

Set LoRa weight to produce:

• Heigher weight: more cultural capital

• Lower weight: less cultural capital

Training conditioning:

Positive:

"extremely high culture person, high culture, highbrow culture person, connoisseur, cultured person, extremely educated person"

Negative:

"extremely low culture person, low culture, lowbrow culture person, uncultured person, extremely uneducated person, extremely Illiterate person"

Tested on v1.5, RealisticVision, EpicRealism

Trained using: https://github.com/ostris/ai-toolkit

Acknowledgments:

I am grateful to Ostris for their outstanding AI Toolkit, which served as the foundation and inspiration for this Slider LoRa. Kindly acknowledge and credit Ostris for their invaluable contributions to the AI community.

If you like my work and would like to support me, you can buy me a coffee!

Any feedback, suggestion or criticism is appreciated.