A novel compositional approach called DSE- Diffusion Score Equilibrium that enables few-shot learning for novel skills by utilizing a combination of base policy priors is presented. Our method is based on probabilistically composing diffusion policies to better model the few-shot demonstration data-distribution than any individual policy. By using our few-shot learning approach DSE, we show that we are able to achieve a reduction of over 30% in MMD distance across skills and number of demonstrations. Moreover, we show the utility of our approach through real world experiments by teaching novel trajectories to a robot in 5 demonstrations.