Despite　advances　in　the　use　of　the　strategy　of　pre-training　then　fine-tuning　in　low-level　vision　tasks,　the　increasing　size　of　models　presents　significant　challenges　for　this　paradigm,　particularly　in　terms　of　training　time　and　memory　consumption.　In　addition,　unsatisfactory　results　may　occur　when　pre-trained　single-image　models　are　directly　applied　to　a　multi-image　domain.　In　this　paper,　we　propose　an　efficient　method　for　transferring　a　pre-trained　single-image　super-resolution　transformer　network　to　the　domain　of　stereo　image　super-resolution　(SteISR)　using　a　parameter-efficient　fine-tuning　approach.　Specifically,　the　concept　of　stereo　adapters　and　spatial　adapters　are　introduced,　which　are　incorporated　into　the　pre-trained　single-image　super-resolution　transformer　network.　Subsequently,　only　the　inserted　adapters　are　trained　on　stereo　datasets.　Compared　with　the　classical　full　fine-tuning　paradigm,　our　method　can　effectively　reduce　training　time　and　memory　consumption　by　57%　and　15%,　respectively.　Moreover,　this　method　allows　us　to　train　only　4.8%　of　the　original　model　parameters,　achieving　state-of-the-art　performance　on　four　commonly　used　SteISR　benchmarks.　This　technology　is　expected　to　improve　stereo　image　resolution　in　various　fields　such　as　medical　imaging　and　autonomous　driving,　thereby　indirectly　enhancing　the　accuracy　of　depth　estimation　and　object　recognition　tasks.