Text-to-image (T2I) diffusion models require large-scale training data to achieve such good performance. Still, they seem to lack a common understanding of semantics such as spatial composition, and spurious correlations raising ethical concerns. Data and model size do not matter in learning better semantics; instead, they seem to hurt the model. Recent works have shown the few-shot concept learning abilities of T2I models on simple concepts like cat or dog. Following the line of research, we introduce in this chapter utilizing Concept Algebra for learning new concepts in a resource-efficient way.

To do that, we introduce three works focusing on concept learning to show its effectiveness: (1) Create a benchmark for large-scale evaluations of concept learning methodologies, (2) Reduce ethical biases via Concept Algebra via few-shot concept learning, and (3) Learn spatial relationships via few-shot concept adaptation. Through this research, we describe the efforts to create few-shot synthetic data that is both robust and reduces biases present in various forms.