A　daylight　performance　evaluation　at　the　early　design　stage　is　essential　for　a　building　morphology　design　and　optimization,　having　a　tremendous　influence　on　energy　consumption　and　indoor　environments.　Considering　the　complicated　input　parameters,　time　and　computational　cost　of　the　simulation　tools,　although　proxy　models　based　on　various　machine　learning　algorithms　have　been　developed,　they　are　limited　to　certain　building　forms　described　based　on　the　selected　parameters.　In　this　study,　proxy　models　of　a　daylight　simulation　for　general　floorplans　are　proposed　based　on　convolutional　neural　network　(CNN)　and　generative　adversarial　network　(GAN).　ResNet　(CNN)　and　pix2pix　(GAN)　are　applied　to　predict　static　and　annual　daylight　metrics　(uniformity,　mean　lux,　success　rate,　sDA,　and　UDI)　and　illuminance　distribution　in　space,　respectively.　Geometry　information　is　embedded　in　the　image　structure,　and　a　grid-based　simulation　are　conducted　as　the　ground　truth.　Two　datasets　composed　of　real　floorplan　cases　and　parametric　rooms　were　tested　in　the　experiments.　ResNet　obtains　the　best　R-2　of　0.959　and　an　MSE　of　0.008　on　the　real　case　dataset　for　daylight　uniformity.　In　addition,　pix2pix　generates　visualization　results　close　to　the　simulation　with　an　SSIM　of　0.90　in　the　test　set　within　a　period　of　1　s　and　provides　real-time　intuitive　feedback　for　designers.　The　results　show　the　possibility　of　using　deep　neural　networks　to　extract　features　from　general　building　forms　and　build　predictive　models,　which　can　be　integrated　into　automatic　form-finding　and　design　optimization.