Style　transfer　methods　render　various　artistic　styles　to　a　natural　image　through　the　extraction　and　transfer　of　textural　features.　Existing　neural　style　transfer　methods　often　rely　on　CNNs　to　extract　image　features　and　tend　to　suffer　from　feature　leakage　and　content　distortion　due　to　limited　receptive　fields.　Transformer-based　style　transfer　methods　outperform　CNN-based　methods　by　learning　the　global　information　of　image　through　self-　attention　mechanism.　However,　local　features　are　ignored　and　details　are　lost　since　the　semantic　information　of　images　is　not　taken　into　account.　To　address　this　critical　issue,　this　paper　proposes　a　novel　style　transfer　framework　based　on　region-aware　transformer　(STRAT).　We　integrate　the　CNN　based　short-range　branch　with　the　transformer-based　long-range　branch　to　extract　both　local　and　non-local　features　to　achieve　region-adaptive　texture　transfer　with　two　region-aware　attention　modules,　respectively.　Specifically,　we　utilize　the　SNR　metric　and　masks　as　guide　to　propose　the　SNR-guided　attention　module　and　mask-guided　cross　attention　module　to　enable　region-varying　feature　extraction　and　adaptive　texture　transfer,　respectively.　Extensive　experimental　results　demonstrate　that　our　proposed　method　outperforms　the　state-of-the-arts　methods　in　terms　of　subjective　and　objective　results.