4D　light　field　(LF)　images　can　be　encoded　from　3D　scenes　by　LF　cameras,　have　highly　coupled　spatial　and　angular　information　and　cannot　be　directly　utilized.　To　solve　this　problem,　a　disentangling　mechanism　is　proposed　in　the　DistgSSR　method　[1].　Despite　delivering　encouraging　results　of　DistgSSR,　the　parameters　of　different　feature　extractors　in　the　Distg-Block　of　DistgSSR　are　manually　tweaked.　Moreover,　the　concatenated　features　generated　by　different　extractors　should　be　fused　by　two　convolutions,　which　will　consume　unnecessary　resources.　To　address　this　issue,　we　propose　a　dimension-invariant　disentangling　(DiD)　model,　which　is　a　plug-and-play　model　and　can　adaptively　allocate　the　dimension　of　the　features　to　fit　the　residual-in-residual　structure.　The　computational　complexity,　GPU_Memory　and　FLOPs　of　our　DiD　model　are　further　reduced　without　losing　performance　compared　with　the　DistgSSR.　Furthermore,　our　DiD　model　can　be　adapted　to　features　of　different　sizes　and　may　be　applied　to　some　networks　for　other　tasks.　To　further　improve　the　reconstruction　result,　we　integrate　a　multi-stage　spectral　dimensional　Transformer　(MST++)　into　the　DiD　model　to　propose　the　DiD-MST++　for　LF　super-resolution.　Extensive　experimental　results　on　five　public　LF　datasets　demonstrate　that　the　DiD　and　DiD-MST++　can　outperform　state-of-the-art　methods　in　terms　of　objective　and　subjective　quality.　©　2023　IEEE.