Hyperspectral　images　(HSIs)　can　reflect　the　spectral　characteristics　of　objects　in　multiple　bands,　which　can　be　used　in　various　tasks,　including　classification,　material　detection　and　identification,　and　geological　exploration.　However,　due　to　hardware　limitations,　spatial　data　have　commonly　been　partially　discarded　to　obtain　more　spectral　information.　Therefore,　the　enhancement　of　spatial　resolution　is　often　contemplated　through　the　application　of　super-resolution　algorithms.　In　view　of　this,　this　study　proposes　a　diffusion　model-assisted　multi-scale　spectral　attention　network　(DMSANet)　to　increase　the　HSI　resolution　in　the　spatial　dimension　while　preserving　spectral　information　as　much　as　possible.　For　the　first　time,　a　diffusion　model　is　combined　with　deep　networks　to　solve　the　HSI　super-resolution　problem,　which　enhances　the　spatial　texture　details　of　the　output　image　using　a　layer-by-layer　super-resolution　mechanism　of　Markov　chains.　In　addition,　a　multi-scale　attention　block　that　can　integrate　multiple　receptive　fields　to　extract　spectral　features　of　HSIs　is　designed,　which　enhances　spectral　information　details.　Extensive　evaluations　and　comparisons　on　three　benchmark　datasets　demonstrate　that　the　proposed　DMSANet　can　achieve　superior　performance　compared　with　the　existing　methods.　Authors