We　propose　a　novel　Text-to-Image　Generation　Network,　Attention-bridged　Modal　Interaction　Generative　Adversarial　Network　(AMI-GAN),　to　better　explore　modal　interaction　and　perception　for　high-quality　image　synthesis.　The　AMI-GAN　contains　two　novel　designs:　an　Attention-bridged　Modal　Interaction　(AMI)　module　and　a　Residual　Perception　Discriminator　(RPD).　In　AMI,　we　mainly　design　a　multi-scale　attention　mechanism　to　exploit　semantics　alignment,　fusion,　and　enhancement　between　text　and　image,　to　better　refine　details　and　context　semantics　of　the　synthesized　image.　In　RPD,　we　design　a　multi-scale　information　perception　mechanism　with　our　proposed　novel　information　adjustment　function,　to　encourage　the　discriminator　to　better　perceive　visual　differences　between　the　real　and　synthesized　image.　Consequently,　the　discriminator　will　drive　the　generator　to　improve　the　visual　quality　of　the　synthesized　image.　Besides,　based　on　these　novel　designs,　we　can　design　two　versions,　a　single-stage　generation　framework　(AMI-GAN-S),　and　a　multi-stage　generation　framework　(AMI-GAN-M),　respectively.　The　former　can　synthesize　high-resolution　images　because　of　its　low　computational　cost;　the　latter　can　synthesize　images　with　realistic　detail.　Experimental　results　on　two　widely　used　T2I　datasets　showed　that　our　AMI-GANs　achieve　competitive　performance　in　T2I　task.