What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives
The study investigated design choices affecting compositional generalization in visual generative models by varying three axes: tokenizer, generative model, and conditioning signal. Key findings show that continuous training objectives are vital for robust composition, while discrete objectives hinder it. Furthermore, complete conditioning on generating factors during training is critical; partial or quantized information leads to weaker compositions.