Usually Transformers perform global self-attention, where the relationships between a token and all other tokens are computed. The global computation leads to quadratic complexity with respect to the number of tokens. Here, as the original paper suggests, we compute self-attention within local windows, in a non-overlapping manner. Global self-attention leads to quadratic computational complexity in the number of patches, whereas window-based self-attention leads to linear complexity and is easily scalable.