Image　downscaling　has　been　a　classical　problem　and　has　recently　been　linked　to　super-resolution　(SR).　In　this　paper,　we　aim　to　propose　a　learning-based　image　downscaling　model,　FastDownscaler,　which　can　efficiently　produce　low-resolution　(LR)　images　that　not　only　preserve　the　rich　details　of　the　original　high-resolution　images　but　also　be　highly　restorable　for　existing　SR　models.　We　first　present　two　separate　lightweight　networks　with　different　upsampling　losses,　the　bilinear　loss　and　the　bicubic　loss,　which　are　better　for　SR　restoration　and　LR　downscaling,　respectively.　To　produce　versatile　LR　images,　we　then　propose　to　distill　bilinear　loss　guided　network　with　bicubic　loss　guided　one.　To　our　best　knowledge,　we　establish　the　first　image　downscaling　quality　assessment　dataset　to　evaluate　the　downscaling　performance.　Experimental　results　demonstrate　the　superior　performance　of　the　proposed　model　on　image　downscaling　and　SR.　Furthermore,　our　model　can　achieve　over　600　FPS　for　downscaling　a　1920\times　1280　image.　©　2022　IEEE.