Compose Once, Sample Many: Amortized Composition of Diffusion Models

Composing pretrained diffusion models enables flexible inference-time generation without retraining, but existing composition methods are expensive when many samples are required from the same target distribution: each new sample repeatedly queries all constituent models throughout a long sampling trajectory. We introduce AmCo (Amortized Composition), a two-stage framework that pays this composition cost only for a small set of initial samples and reuses the resulting information for subsequent generation. In Stage 1, AmCo applies an existing composition method to obtain a small set of anchor samples together with their constituent density values. In Stage 2, these anchors are used to approximate the clean prediction of the target composed distribution, while a single constituent model provides a finite-sample correction. Consequently, Stage 2 requires only one constituent-model evaluation per sampling step, and supports substantially shorter deterministic sampling trajectories. Experiments on classifier-free-guided image generation and product-of-experts ligand generation show that AmCo preserves or improves generation quality and diversity while substantially reducing inference cost.