  We propose a novel method to construct large-scale boundary-identical
microstructure datasets with high attribute coverage for highly efficient
multiscale design. Central to our technique is using a deep generative model to
generate microstructures under the two conditions, including the specified
boundary and homogenized elastic tensor. We achieve the desired dataset by
alternately adding microstructures with diverse attributes constructed by the
deep generative model into the dataset and retraining the deep generative
model. Sixteen compatible metamaterial datasets with high attribute coverage
are constructed by our method. We demonstrate the effectiveness and
practicability of the obtained datasets over various multiscale design
examples. Specifically, in the design of a mechanical cloak, we utilize
macrostructures with $30 \times 30$ elements and microstructures filled with
$256 \times 256$ elements. The entire reverse design process is completed
within one minute, significantly enhancing the efficiency of the multiscale
optimization.
