Infrared　small　target　detection　(IRSTD)　aims　to　segment　small　targets　from　infrared　clutter　background.　Existing　methods　mainly　focus　on　discriminative　approaches,　i.e.,　a　pixel-level　front-background　binary　segmentation.　Since　infrared　small　targets　are　small　and　low　signal-to-clutter　ratio,　empirical　risk　has　few　disturbances　when　a　certain　false　alarm　and　missed　detection　exist,　which　seriously　affect　the　further　improvement　of　such　methods.　Motivated　by　the　dense　prediction　generative　methods,　in　this　article,　we　compensate　pixel-level　discriminant　with　mask　posterior　distribution　modeling.　Specifically,　we　propose　a　diffusion　model　framework　for　IRSTD.　This　generative　framework　maximizes　the　posterior　distribution　of　the　small　target　mask　to　surmount　the　performance　bottleneck　associated　with　minimizing　discriminative　empirical　risk.　This　transition　from　the　discriminative　paradigm　to　generative　one　enables　us　to　bypass　the　target-level　insensitivity.　Furthermore,　we　design　a　low-frequency　isolation　in　wavelet　domain　to　suppress　the　interference　of　intrinsic　infrared　noise　on　the　diffusion　noise　estimation.　The　low-frequency　component　of　the　infrared　image　in　the　wavelet　domain　is　processed　by　a　neural　network,　and　the　high-frequency　component　is　utilized　to　restore　the　targets　information,　to　estimate　the　residuals　of　the　enhanced　features.　Experiments　show　that　the　proposed　method　achieves　competitive　performance　gains　over　state-of-the-art　methods　on　NUAA-SIRST,　NUDT-SIRST,　and　IRSTD-1　k　datasets.