Many　existing　generative　adversarial　networks　(GANs)　lack　effective　semantic　modeling,　leading　to　unnatural　local　details　and　blurring　in　generated　images.　In　this　work,　based　on　DivCo,　we　propose　a　Symmetric　Dual-Attention　Generative　Adversarial　Network　(DivCo-SDAGAN)　with　channel　and　spatial　feature　fusion　in　which　the　Dual-Attention　Module　(DAM)　is　introduced　to　strengthen　the　feature　representation　ability　of　the　network　to　synthesize　photo-realistic　images　with　more　natural　local　details.　The　Channel　Weighted　Aggregation　Module　(CWAM)　and　the　Spatial　Attention　Module　(SAM)　of　the　DAM　are　designed　to　capture　the　semantic　information　of　channel　dimension　and　spatial　dimension,　respectively,　and　they　can　be　easily　integrated　into　other　GANs-based　models.　Extensive　experiments　show　that　the　proposed　DivCo-SDAGAN　can　produce　more　diverse　images　under　the　same　input,　achieving　more　satisfactory　results　than　other　existing　methods.　©　2022　ACM.