The efficiency gains of BinaryDM can be leveraged to enable the deployment of diffusion models on resource-constrained edge devices for real-world applications in several ways. Firstly, the significant reduction in FLOPs and model size achieved by BinaryDM allows for faster and more efficient inference on edge devices with limited computational resources. This can lead to improved performance and responsiveness of applications utilizing diffusion models in edge computing scenarios. Secondly, the compact and efficient nature of BinaryDM makes it well-suited for deployment in edge devices with restricted storage capacity. By reducing the model size and memory requirements, BinaryDM enables the deployment of complex generative models on edge devices without compromising performance. Overall, the efficiency gains of BinaryDM open up opportunities for deploying diffusion models in a wide range of resource-constrained edge applications, including IoT devices, mobile devices, and embedded systems.