Image　hiding　aims　to　conceal　one　or　more　secret　images　within　a　cover　image　of　the　same　resolution.　Due　to　strict　capacity　requirements,　image　hiding　is　commonly　called　large-capacity　steganography.　In　this　paper,　we　propose　StegFormer,　a　novel　autoencoder-based　image-hiding　model.　StegFormer　can　conceal　one　or　multiple　secret　images　within　a　cover　image　of　the　same　resolution　while　preserving　the　high　visual　quality　of　the　stego　image.　In　addition,　to　mitigate　the　limitations　of　current　steganographic　models　in　real-world　scenarios,　we　propose　a　normalizing　training　strategy　and　a　restrict　loss　to　improve　the　reliability　of　the　steganographic　models　under　realistic　conditions.　Furthermore,　we　propose　an　efficient　steganographic　capacity　expansion　method　to　increase　the　capacity　of　steganography　and　enhance　the　efficiency　of　secret　communication.　Through　this　approach,　we　can　increase　the　relative　payload　of　StegFormer　to　96　bits　per　pixel　without　any　training　strategy　modifications.　Experiments　demonstrate　that　our　StegFormer　outperforms　existing　state-of-the-art　(SOTA)　models.　In　the　case　of　single-image　steganography,　there　is　an　improvement　of　more　than　3　dB　and　5　dB　in　PSNR　for　secret/recovery　image　pairs　and　cover/stego　image　pairs.　Copyright　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.