This　paper　aims　to　resolve　the　challenging　problem　of　wide-angle　novel　view　synthesis　from　a　single　image,　a.k.a.　wide-angle　3D　photography.　Existing　approaches　rely　on　local　context　and　treat　them　equally　to　inpaint　occluded　RGB　and　depth　regions,　which　fail　to　deal　with　large-region　occlusion　(i.e.,　observing　from　an　extreme　angle)　and　foreground　layers　might　blend　into　background　inpainting.　To　address　the　above　issues,　we　propose　Diffuse3D　which　employs　a　pre-trained　diffusion　model　for　global　synthesis,　while　amending　the　model　to　activate　depth-aware　inference.　Our　key　insight　is　to　alter　the　convolution　mechanism　in　the　denoising　process.　We　inject　depth　information　into　the　denoising　convolution　operation　with　bilateral　kernels,　i.e.,　a　depth　kernel　and　a　spatial　kernel,　to　consider　layered　correlations　among　pixels.　In　this　way,　foreground　regions　are　overlooked　in　background　inpainting　and　only　pixels　close　in　depth　are　leveraged.　On　the　other　hand,　we　propose　a　global-local　balancing　approach　to　maximize　both　contextual　understandings.　Extensive　experiments　demonstrate　that　our　approach　outperforms　state-of-the-art　methods　in　novel　view　synthesis,　especially　in　wide-angle　scenarios.　More　importantly,　our　method　does　not　require　any　training　and　is　a　plug-and-play　module　that　can　be　integrated　with　any　diffusion　model.　Our　code　can　be　found　at　https://github.com/yutaojiang1/Diffuse3D.