Single　Image　Super-Resolution　(SISR)　aims　to　reconstruct　a　high-resolution　image　from　its　corresponding　low-resolution　input.　A　common　technique　to　enhance　the　reconstruction　quality　is　Non-Local　Attention　(NLA),　which　leverages　self-similar　texture　patterns　in　images.　However,　we　have　made　a　novel　finding　that　challenges　the　prevailing　wisdom.　Our　research　reveals　that　NLA　can　be　detrimental　to　SISR　and　even　produce　severely　distorted　textures.　For　example,　when　dealing　with　severely　degrade　textures,　NLA　may　generate　unrealistic　results　due　to　the　inconsistency　of　non-local　texture　patterns.　This　problem　is　overlooked　by　existing　works,　which　only　measure　the　average　reconstruction　quality　of　the　whole　image,　without　considering　the　potential　risks　of　using　NLA.　To　address　this　issue,　we　propose　a　new　perspective　for　evaluating　the　reconstruction　quality　of　NLA,　by　focusing　on　the　sub-pixel　level　that　matches　the　pixel-wise　fusion　manner　of　NLA.　From　this　perspective,　we　provide　the　approximate　reconstruction　performance　upper　bound　of　NLA,　which　guides　us　to　design　a　concise　yet　effective　Texture-Fidelity　Strategy　(TFS)　to　mitigate　the　degradation　caused　by　NLA.　Moreover,　the　proposed　TFS　can　be　conveniently　integrated　into　existing　NLA-based　SISR　models　as　a　general　building　block.　Based　on　the　TFS,　we　develop　a　Deep　Texture-Fidelity　Network　(DTFN),　which　achieves　state-of-the-art　performance　for　SISR.　Our　code　and　a　pre-trained　DTFN　are　available　on　GitHub(dagger)　for　verification.