Severity-Controlled Manipulation of Text-to-Image Generative Model Biases

This work proposes a computationally efficient technique to manipulate the biases of text-to-image generative models by targeting the embedded language models. The method enables precise control over the severity of output manipulation through vector algebra-based embedding interpolation and extrapolation.