CrysText enhances computational efficiency while significantly improving the model's capability to produce physically plausible crystal structures. Evaluations on the MP-20 benchmark dataset demonstrate high structure match rates and effective RMSE metrics, highlighting its ability to generate crystal structures that accurately adhere to specified compositions and crystal symmetries. Furthermore, CrysText demonstrates its potential to generate stable crystal structures by conditioning on properties such as energy above the hull, offering a more accessible and targeted approach to inverse materials design.