In　this　paper,　a　cross-resolution　acceleration　method　for　topology　optimization　is　proposed　based　on　deep　learning　aiming　at　achieving　precise　and　high-efficiency　geometrically　non-linear　structure　design.　We　develop　a　cross-resolution　Pix2pix　neural　network　(CR-Pix2pix　NN)　to　build　the　high-dimensional　mapping　between　the　low-resolution　intermediate　configuration　(IC)　and　the　corresponding　high-resolution　optimized　configuration.　Architecture　of　CR-Pix2pix　NN　is　composed　of　a　cross-resolution　generator　and　Markovian　discriminator.　The　cross-resolution　geometrically　non-linear　dataset　utilized　to　train　the　deep　learning　model　is　created　by　solving　stress　constrained　topology　optimization　model　established　by　independent　continuous　mapping　(ICM)　method.　The　pre-trained　CR-Pix2pix　NN　is　capable　of　accurately　predicting　the　high-resolution　optimized　configuration　by　inputting　the　low-resolution　IC　with　the　only　one　iteration　step.　Furthermore,　the　hyperparameters　of　the　developed　model　are　discussed　to　ensure　the　performance　in　accuracy　and　computational　efficiency.　The　proposed　method　can　be　generalized　to　other　precise　topology　optimization　scenarios　and　engineering　design.