Token compression in diffusion models can serve both generation and classification if you preserve different frequency components: keep high-frequency details for texture/edges and low/mid-frequency information for semantic understanding.
BiGain is a method that speeds up diffusion models while keeping both image generation and classification working well. It uses frequency-aware token compression—separating fine details from overall structure—to decide which tokens to merge or remove, maintaining visual quality and classification accuracy simultaneously.