Automatically　generated　residential　floor　plans　using　Artificial　Intelligence　that　lower　the　skill　barriers　in　and　facilitate　non-professional　residential　design,　have　become　a　significant　topic.　However,　in　previous　studies,　the　limitations　of　RFP　generative　models　have　exhibited　low　controllability　in　outputs　and　limited　flexibility　in　input　conditions.　In　this　study,　a　multi-conditional,　two-stage　generative　model,　FloorplanDiffusion,　was　developed　to　address　these　shortcomings.　Based　on　Denoising　Diffusion　Probabilistic　Models,　a　new　model　structure　was　established,　allowing　human　designers　to　intervene　for　enhanced　controllability.　Furthermore,　we　implemented　a　multi-condition　model　input　with　structured　information　using　images,　thus　significantly　enhancing　the　model＇s　input　flexibility.　Finally,　through　experiments　we　demonstrated　that　our　model　flexibly　generates　high-quality,　diverse,　and　controllable　results.　A　Turing　test　indicated　that　our　model　has　the　capacity　of　human　experts.