A　generative　adversarial　network　with　deep　fusion　attention　(DFA-GAN)　was　proposed,　using　multiple　loss　functions　as　constraints,　to　address　the　issues　of　poor　image　quality　and　inconsistency　between　text　descriptions　and　generated　images　in　traditional　text-to-image　generation　methods.　A　two-stage　image　generation　process　was　employed　with　a　single-level　generative　adversarial　network　(GAN)　as　the　backbone.　An　initial　blurry　image　which　was　generated　in　the　first　stage　was　fed　into　the　second　stage,　and　high-quality　image　regeneration　was　achieved　to　enhance　the　overall　image　generation　quality.　During　the　first　stage,　a　visual-text　fusion　module　was　designed　to　deeply　integrate　text　features　and　image　features,　and　text　information　was　adequately　fused　during　the　image　sampling　process　at　different　scales.　In　the　second　stage,　an　image　generator　with　an　improved　Vision　Transformer　as　the　encoder　was　proposed　to　fully　fuse　image　features　with　text　description　word　features.　Quantitative　and　qualitative　experimental　results　showed　that　the　proposed　method　outperformed　other　mainstream　models　in　terms　of　image　quality　improvement　and　alignment　with　text　descriptions.　©　2024　Zhejiang　University.　All　rights　reserved.