The development of de novo protein design method is crucial for widespread applications in biology and chemistry. Protein backbone diffusion aims to generate designable protein structures with high efficiency. Although there have been great strides in protein structure prediction, applying these methods to protein diffusion has been challenging and inefficient. We introduce Proteus, an innovative approach that uses graph-based triangle methods and a multi-track interaction network. Proteus demonstrated cutting-edge designability and efficiency in computational evaluations. We tested the reliability of the model by experimental characterization. Our analysis indicates that from both computational and experimental perspectives, it is able to generate proteins with a remarkably high success rate. We believe Proteus's capacity to rapidly create highly designable protein backbone without the need for pre-training techniques will greatly enhance our understanding of protein structure diffusion and contribute to advances in protein design.