This　article　presents　a　new　text-to-image　(T2I)　generation　model,　named　distribution　regularization　generative　adversarial　network　(DR-GAN),　to　generate　images　from　text　descriptions　from　improved　distribution　learning.　In　DR-GAN,　we　introduce　two　novel　modules:　a　semantic　disentangling　module　(SDM)　and　a　distribution　normalization　module　(DNM).　SDM　combines　the　spatial　self-attention　mechanism　(SSAM)　and　a　new　semantic　disentangling　loss　(SDL)　to　help　the　generator　distill　key　semantic　information　for　the　image　generation.　DNM　uses　a　variational　auto-encoder　(VAE)　to　normalize　and　denoise　the　image　latent　distribution,　which　can　help　the　discriminator　better　distinguish　synthesized　images　from　real　images.　DNM　also　adopts　a　distribution　adversarial　loss　(DAL)　to　guide　the　generator　to　align　with　normalized　real　image　distributions　in　the　latent　space.　Extensive　experiments　on　two　public　datasets　demonstrated　that　our　DR-GAN　achieved　a　competitive　performance　in　the　T2I　task.　The　code　link:　https://github.com/Tan-H-C/DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation.