Lace　texture,　as　a　manually　designed　texture　image,　needs　to　possess　a　series　of　essential　aesthetic　characteristics,　such　as　periodicity,　symmetry,　and　blank-leaving　in　artistic　design　creation.　It　requires　human　designers　to　spend　a　lot　of　time　and　effort,　so　it　is　necessary　to　apply　generative　models　to　generate　lace　images.　In　image　generation　tasks,　compared　to　models　such　as　DCGAN,　CycleGAN,　and　ProGAN,　although　images　generated　using　StyleGAN2　perform　well　in　terms　of　resolution,　texture　details,　and　periodicity,　they　still　perform　poorly　in　terms　of　symmetry　in　lace　images.　To　address　the　above　issues,　this　article　proposes　an　improved　model　SStyleGAN　(Symmetry　StyleGAN)　based　on　StyleGAN2.　In　terms　of　discriminators,　in　order　to　enhance　the　attention　of　the　proposed　model　to　image　symmetry,　we　have　added　a　symmetry　discriminator,　that　is,　SStyleGAN　adopts　a　dual　discriminator　structure;　In　terms　of　generator,　in　order　to　improve　the　similarity　of　the　feature　maps　on　the　left　and　right　sides　of　the　lace　image,　this　paper　adds　a　mean　square　error　loss　term　based　on　the　loss　function　of　StyleGAN2;　In　terms　of　noise　input,　in　order　to　control　the　symmetry　of　the　lace　image　at　details　such　as　lace　edges,　the　noise　of　the　StyleGAN2　model　is　modified　to　a　symmetrical　structure,　so　that　the　noise　input　itself　has　symmetry.　In　addition　to　the　commonly　used　FID　(Fréchet　Insertion　Distance)　in　the　generative　model,　we　also　used　the　SSIM　(Structural　Similarity)　metric　for　the　evaluation　of　the　experimental　results　in　this　article　to　detect　the　symmetry　of　the　generated　images.　The　experimental　results　show　that　compared　to　the　lace　images　generated　by　the　StyleGAN2　model,　the　lace　images　generated　by　the　model　proposed　in　this　paper　not　only　inherit　the　advantages　of　the　former,　but　also　have　symmetry　characteristics.　©　2024　SPIE.