Diffusion models have shown tremendous results in image generation. However due to the iterative nature of the diffusion process and its reliance on classifier-free guidance inference times are slow. In this paper we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half and only requires 1% trainable parameters of the base model. Furthermore once trained our guide model can be applied to various fine-tuned domain-specific versions of the base diffusion model without the need for additional training: this" plug-and-play" functionality drastically improves inference computation while maintaining the visual fidelity of generated images. Empirically we show that our approach is able to produce visually appealing results and achieve a comparable FID score to the teacher with as few as 8 to 16 steps.