In the field of acoustic simulation, widely applied methods rely on the impulse response (IR) and its convolution relationships. However, most deep learning-based approaches for generating IRs are limited to monaural IRs. Some methods for generating binaural IRs require specialized binaural IR datasets, which are costly to collect and difficult to obtain under extreme conditions, such as underwater environments. Therefore, this paper introduces a low-cost and practical technique, Bi-IRNet, which guides various IR generation models to produce corresponding binaural IRs using positional information as input. Our method leverages transformer networks and the Head-Related Transfer Function (HRTF) database to train a binaural IR generation guidance module. This module can be easily embedded into other IR generation models, enabling end-to-end generation of spatially aware binaural IRs. With this module, IR generation models can produce spatial binaural IRs without the need for a binaural IR dataset, significantly reducing the cost of deep learning-based binaural IR generation.