\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55，\u57fa\u4e8eAI\u7684\u56fe\u50cf\u4fee\u590d（Image Inpainting）\u6280\u672f\u9010\u6e10\u6210\u719f，\u4e3a\u81ea\u52a8\u5316\u3001\u9ad8\u8d28\u91cf\u53bb\u6c34\u5370\u63d0\u4f9b\u4e86\u65b0\u7684\u89e3\u51b3\u65b9\u6848\u3002\u672c\u6587\u5c06\u4ee5\u7b14\u8005\u6700\u8fd1\u5f00\u53d1\u5e76\u5f00\u6e90\u7684\u300c\u730e\u624b\u53bb\u6c34\u5370\u300d\u5fae\u4fe1\u5c0f\u7a0b\u5e8f\u4e3a\u4f8b，\u4ece\u6280\u672f\u89d2\u5ea6\u6df1\u5165\u63a2\u8ba8AI\u53bb\u6c34\u5370\u7cfb\u7edf\u7684\u8bbe\u8ba1\u601d\u8def\u3001\u6838\u5fc3\u7b97\u6cd5\u3001\u5de5\u7a0b\u5b9e\u73b0\u4ee5\u53ca\u6027\u80fd\u4f18\u5316\u7b56\u7565\u3002<\/p>\n
\u672c\u6587\u6240\u6709\u6280\u672f\u8ba8\u8bba\u5747\u57fa\u4e8e\u516c\u5f00\u7684\u5b66\u672f\u7814\u7a76\u548c\u5f00\u6e90\u5b9e\u73b0，\u65e8\u5728\u6280\u672f\u4ea4\u6d41\u4e0e\u5b66\u4e60\u5206\u4eab\u3002<\/p>\n
\n
\u4e8c\u3001AI\u53bb\u6c34\u5370\u6838\u5fc3\u6280\u672f\u539f\u7406<\/h3>\n2.1 \u56fe\u50cf\u53bb\u6c34\u5370\u7684\u672c\u8d28<\/h4>\n
\u4ece\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u89d2\u5ea6\u6765\u770b，\u53bb\u6c34\u5370\u672c\u8d28\u4e0a\u662f\u4e00\u4e2a**\u56fe\u50cf\u4fee\u590d（Image Inpainting）**\u95ee\u9898\u3002\u5176\u76ee\u6807\u662f：\u7ed9\u5b9a\u4e00\u5f20\u5e26\u6709\u6c34\u5370\/\u906e\u6321\u533a\u57df\u7684\u56fe\u50cf，\u6839\u636e\u5468\u56f4\u7684\u50cf\u7d20\u4fe1\u606f，\u667a\u80fd\u5730"\u731c\u6d4b"\u88ab\u906e\u6321\u533a\u57df\u539f\u672c\u5e94\u8be5\u662f\u4ec0\u4e48\u6837\u5b50，\u5e76\u751f\u6210\u81ea\u7136\u3001\u8fde\u8d2f\u7684\u4fee\u590d\u7ed3\u679c\u3002<\/p>\n
\u4f20\u7edf\u7684\u56fe\u50cf\u4fee\u590d\u65b9\u6cd5（\u5982\u57fa\u4e8ePatchMatch\u7684\u65b9\u6cd5\u3001\u57fa\u4e8e\u504f\u5fae\u5206\u65b9\u7a0bPDE\u7684\u65b9\u6cd5）\u5728\u5904\u7406\u5c0f\u9762\u79ef\u3001\u7eb9\u7406\u7b80\u5355\u7684\u533a\u57df\u65f6\u6548\u679c\u5c1a\u53ef，\u4f46\u9762\u5bf9\u590d\u6742\u573a\u666f（\u5982\u4eba\u7269\u9762\u90e8\u3001\u7cbe\u7ec6\u7eb9\u7406\u3001\u6e10\u53d8\u80cc\u666f）\u65f6\u5f80\u5f80\u529b\u4e0d\u4ece\u5fc3，\u5bb9\u6613\u51fa\u73b0\u6a21\u7cca\u3001\u7eb9\u7406\u4e0d\u81ea\u7136\u7b49\u95ee\u9898\u3002<\/p>\n
2.2 \u6df1\u5ea6\u5b66\u4e60\u65f6\u4ee3\u7684\u89e3\u51b3\u65b9\u6848<\/h4>\n
\u8fd1\u5e74\u6765，\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u56fe\u50cf\u4fee\u590d\u65b9\u6cd5\u53d6\u5f97\u4e86\u7a81\u7834\u6027\u8fdb\u5c55\u3002\u4e3b\u6d41\u7684\u6280\u672f\u8def\u7ebf\u5305\u62ec：<\/p>\n
2.2.1 \u57fa\u4e8e\u751f\u6210\u5bf9\u6297\u7f51\u7edc（GAN）\u7684\u65b9\u6cd5<\/h5>\n
\u4ee3\u8868\u6027\u5de5\u4f5c\u5982DeepFill v1\/v2\u3001Partial Convolution\u7b49\u3002\u8fd9\u7c7b\u65b9\u6cd5\u7684\u6838\u5fc3\u601d\u60f3\u662f：<\/p>\n
- \n
- \u751f\u6210\u5668（Generator）：\u8d1f\u8d23\u6839\u636e\u8f93\u5165\u56fe\u50cf\u548c\u63a9\u7801（mask，\u6807\u8bb0\u9700\u8981\u4fee\u590d\u7684\u533a\u57df）\u751f\u6210\u4fee\u590d\u540e\u7684\u56fe\u50cf<\/li>\n
- \u5224\u522b\u5668（Discriminator）：\u8d1f\u8d23\u5224\u65ad\u4fee\u590d\u7ed3\u679c\u662f"\u771f"（\u771f\u5b9e\u56fe\u50cf）\u8fd8\u662f"\u5047"（\u751f\u6210\u7684\u56fe\u50cf）<\/li>\n
- \u4e24\u8005\u901a\u8fc7\u5bf9\u6297\u8bad\u7ec3，\u6700\u7ec8\u751f\u6210\u5668\u80fd\u591f\u8f93\u51fa\u4ee5\u5047\u4e71\u771f\u7684\u4fee\u590d\u7ed3\u679c<\/li>\n<\/ul>\n
DeepFill v2\u5f15\u5165\u4e86Contextual Attention\u6a21\u5757，\u80fd\u591f\u5728\u4fee\u590d\u65f6"\u53c2\u8003"\u56fe\u50cf\u4e2d\u5176\u4ed6\u533a\u57df\u7684\u76f8\u4f3c\u7eb9\u7406，\u4ece\u800c\u751f\u6210\u66f4\u4e00\u81f4\u3001\u66f4\u81ea\u7136\u7684\u7ed3\u679c\u3002<\/p>\n
2.2.2 \u57fa\u4e8e\u6269\u6563\u6a21\u578b（Diffusion Model）\u7684\u65b9\u6cd5<\/h5>\n
\u968f\u7740Stable Diffusion\u7b49\u6269\u6563\u6a21\u578b\u7684\u5174\u8d77，\u57fa\u4e8e\u6269\u6563\u6a21\u578b\u7684\u56fe\u50cf\u4fee\u590d\u65b9\u6cd5\u4e5f\u9010\u6e10\u6210\u4e3a\u4e3b\u6d41\u3002\u8fd9\u7c7b\u65b9\u6cd5\u7684\u4f18\u52bf\u5728\u4e8e：<\/p>\n
- \n
- \u751f\u6210\u8d28\u91cf\u66f4\u9ad8，\u7ec6\u8282\u66f4\u4e30\u5bcc<\/li>\n
- \u53ef\u4ee5\u7ed3\u5408\u6587\u672c\u63d0\u793a（text prompt）\u5f15\u5bfc\u4fee\u590d\u65b9\u5411<\/li>\n
- \u5bf9\u5927\u9762\u79ef\u7f3a\u5931\u533a\u57df\u7684\u4fee\u590d\u6548\u679c\u66f4\u597d<\/li>\n<\/ul>\n
\u4ee3\u8868\u6027\u5de5\u4f5c\u5305\u62ecStable Diffusion Inpainting\u3001LaMa（Large Mask Inpainting）\u7b49\u3002\u5176\u4e2dLaMa\u91c7\u7528\u4e86\u57fa\u4e8e\u5085\u91cc\u53f6\u5377\u79ef（Fourier Convolutions）\u7684\u67b6\u6784，\u80fd\u591f\u9ad8\u6548\u5730\u6355\u6349\u5168\u5c40\u4e0a\u4e0b\u6587\u4fe1\u606f，\u5728\u5927\u63a9\u7801\u4fee\u590d\u4efb\u52a1\u4e0a\u8868\u73b0\u51fa\u8272\u3002<\/p>\n
2.2.3 \u89c6\u9891\u53bb\u6c34\u5370\u7684\u7279\u6b8a\u6311\u6218<\/h5>\n
\u89c6\u9891\u53bb\u6c34\u5370\u6bd4\u5355\u5f20\u56fe\u7247\u53bb\u6c34\u5370\u66f4\u590d\u6742，\u4e3b\u8981\u96be\u70b9\u5728\u4e8e：<\/p>\n
- \u65f6\u5e8f\u4e00\u81f4\u6027：\u5982\u679c\u9010\u5e27\u72ec\u7acb\u4fee\u590d，\u76f8\u90bb\u5e27\u4e4b\u95f4\u53ef\u80fd\u51fa\u73b0\u95ea\u70c1\u3001\u6296\u52a8\u7b49\u95ee\u9898<\/li>\n
- \u8ba1\u7b97\u91cf\u5de8\u5927：\u77ed\u89c6\u9891\u901a\u5e38\u6709\u51e0\u5341\u5230\u4e0a\u767e\u5e27，\u9010\u5e27\u5904\u7406\u8017\u65f6\u5f88\u957f<\/li>\n
- \u8fd0\u52a8\u4fe1\u606f：\u89c6\u9891\u4e2d\u7684\u7269\u4f53\u548c\u76f8\u673a\u90fd\u5728\u8fd0\u52a8，\u6c34\u5370\u4f4d\u7f6e\u53ef\u80fd\u968f\u65f6\u95f4\u53d8\u5316<\/li>\n
\u9488\u5bf9\u8fd9\u4e9b\u95ee\u9898，\u4e1a\u754c\u63d0\u51fa\u4e86\u591a\u79cd\u89e3\u51b3\u65b9\u6848：<\/p>\n
- \n
- \u5229\u7528\u5149\u6d41（Optical Flow）\u4f20\u9012\u4fe1\u606f，\u4fdd\u8bc1\u65f6\u5e8f\u4e00\u81f4\u6027<\/li>\n
- \u91c7\u7528\u89c6\u9891\u4fee\u590d\u4e13\u7528\u7f51\u7edc（\u5982FGVC\u3001ProPainter\u7b49）<\/li>\n
- \u5173\u952e\u5e27\u4fee\u590d + \u5149\u6d41\u63d2\u503c，\u51cf\u5c11\u8ba1\u7b97\u91cf<\/li>\n<\/ul>\n
\n\u4e09\u3001\u300c\u730e\u624b\u53bb\u6c34\u5370\u300d\u5c0f\u7a0b\u5e8f\u7cfb\u7edf\u67b6\u6784\u8bbe\u8ba1<\/h3>\n
3.1 \u6574\u4f53\u67b6\u6784<\/h4>\n
\u8003\u8651\u5230\u5fae\u4fe1\u5c0f\u7a0b\u5e8f\u7684\u6027\u80fd\u9650\u5236\u548c\u7528\u6237\u4f53\u9a8c\u8981\u6c42，\u6211\u4eec\u91c7\u7528\u4e86**"\u524d\u7aef\u8f7b\u91cf\u4ea4\u4e92 + \u540e\u7aefAI\u8ba1\u7b97"**\u7684\u5206\u79bb\u67b6\u6784：<\/p>\n
\u7528\u6237\u624b\u673a（\u5fae\u4fe1\u5c0f\u7a0b\u5e8f） \u4e91\u7aef\u670d\u52a1\u5668
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u2502 \u2502 \u2502
\n\u2502 \u00b7 \u7528\u6237\u754c\u9762UI \u2502 \u2502 \u00b7 \u89c6\u9891\/\u56fe\u7247\u89e3\u6790 \u2502
\n\u2502 \u00b7 \u94fe\u63a5\u8f93\u5165 \u2502\u2500\u2500\u2500\u2500\u2500\u25b6\u2502 \u00b7 AI\u53bb\u6c34\u5370\u5f15\u64ce \u2502
\n\u2502 \u00b7 \u7ed3\u679c\u9884\u89c8\/\u4fdd\u5b58 \u2502\u25c0\u2500\u2500\u2500\u2500\u2500\u2502 \u00b7 \u6587\u4ef6\u5b58\u50a8CDN \u2502
\n\u2502 \u00b7 \u8fdb\u5ea6\u5c55\u793a \u2502 \u2502 \u00b7 \u4efb\u52a1\u961f\u5217\u7ba1\u7406 \u2502
\n\u2502 \u2502 \u2502 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n\u8fd9\u6837\u8bbe\u8ba1\u7684\u597d\u5904\u662f：<\/p>\n
- \n
- \u964d\u4f4e\u5c0f\u7a0b\u5e8f\u7aef\u6027\u80fd\u538b\u529b：AI\u6a21\u578b\u63a8\u7406\u5168\u90e8\u5728\u4e91\u7aef\u5b8c\u6210<\/li>\n
- \u7edf\u4e00\u7ef4\u62a4\u6a21\u578b：\u6a21\u578b\u8fed\u4ee3\u5347\u7ea7\u65e0\u9700\u7528\u6237\u66f4\u65b0\u5c0f\u7a0b\u5e8f<\/li>\n
- \u652f\u6301\u5927\u6587\u4ef6\u5904\u7406：\u4e0d\u53d7\u5c0f\u7a0b\u5e8f\u5185\u5b58\u548c\u5904\u7406\u80fd\u529b\u9650\u5236<\/li>\n
- \u591a\u7aef\u590d\u7528：\u540c\u4e00\u5957\u540e\u7aef\u670d\u52a1\u53ef\u4ee5\u652f\u6301\u5c0f\u7a0b\u5e8f\u3001H5\u3001App\u7b49\u591a\u79cd\u524d\u7aef<\/li>\n<\/ul>\n
3.2 \u5c0f\u7a0b\u5e8f\u7aef\u6280\u672f\u6808<\/h4>\n
\u524d\u7aef\u91c7\u7528\u5fae\u4fe1\u539f\u751f\u5c0f\u7a0b\u5e8f\u6846\u67b6\u5f00\u53d1，\u4e3b\u8981\u6a21\u5757\u5305\u62ec：<\/p>\n
\n
\u6a21\u5757\u529f\u80fd\u63cf\u8ff0\u6280\u672f\u5b9e\u73b0<\/tr>\n \n\n \u9996\u9875\u6a21\u5757<\/td>\n \u529f\u80fd\u5165\u53e3\u3001\u4f7f\u7528\u5f15\u5bfc\u3001\u5386\u53f2\u8bb0\u5f55<\/td>\n \u539f\u751f\u7ec4\u4ef6 + \u81ea\u5b9a\u4e49\u5bfc\u822a\u680f<\/td>\n<\/tr>\n \n \u89e3\u6790\u6a21\u5757<\/td>\n \u94fe\u63a5\u7c98\u8d34\u3001\u5e73\u53f0\u8bc6\u522b\u3001\u89e3\u6790\u72b6\u6001\u5c55\u793a<\/td>\n \u6b63\u5219\u5339\u914d + WebSocket\u5b9e\u65f6\u63a8\u9001<\/td>\n<\/tr>\n \n \u7ed3\u679c\u6a21\u5757<\/td>\n \u9884\u89c8\u3001\u4fdd\u5b58\u5230\u76f8\u518c\u3001\u5206\u4eab\u529f\u80fd<\/td>\n video\u7ec4\u4ef6 + \u753b\u5e03\u6e32\u67d3<\/td>\n<\/tr>\n \n \u4e2a\u4eba\u4e2d\u5fc3<\/td>\n \u4f7f\u7528\u8bb0\u5f55\u3001\u53cd\u9988\u5165\u53e3<\/td>\n \u672c\u5730\u7f13\u5b58 + \u4e91\u5f00\u53d1\u6570\u636e\u5e93<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u4e0b\u56fe\u662f\u5c0f\u7a0b\u5e8f\u7684\u9996\u9875\u754c\u9762，\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u7c98\u8d34\u4ece\u5404\u5927\u5e73\u53f0\u590d\u5236\u7684\u94fe\u63a5，\u70b9\u51fb"\u5f00\u59cb\u89e3\u6790"\u6309\u94ae\u5373\u53ef\u4e00\u952e\u5b8c\u6210\u53bb\u6c34\u5370\u64cd\u4f5c：
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261007111747-6ac62a5b59ee9.jpg\")
<\/p>\n<\/p>\n\u754c\u9762\u8bbe\u8ba1\u9075\u5faa\u6781\u7b80\u539f\u5219，\u6838\u5fc3\u64cd\u4f5c\u533a\u57df（\u94fe\u63a5\u8f93\u5165\u6846 + \u89e3\u6790\u6309\u94ae）\u4f4d\u4e8e\u89c6\u89c9\u7126\u70b9\u4f4d\u7f6e，\u7528\u6237\u6253\u5f00\u5c0f\u7a0b\u5e8f\u5373\u53ef\u4e0a\u624b，\u96f6\u5b66\u4e60\u6210\u672c\u3002\u5e95\u90e8Tab\u680f\u8fd8\u63d0\u4f9b\u4e86"\u66f4\u591a\u5de5\u5177"\u5165\u53e3，\u96c6\u6210\u4e86\u6296\u97f3\u6743\u91cd\u67e5\u8be2\u3001\u5feb\u624b\u6743\u91cd\u67e5\u8be2\u7b49\u521b\u4f5c\u8005\u5e38\u7528\u7684\u5468\u8fb9\u5de5\u5177，\u63d0\u5347\u4e86\u4ea7\u54c1\u7684\u5b9e\u7528\u6027\u548c\u7528\u6237\u7c98\u6027\u3002<\/p>\n
3.3 \u540e\u7aef\u670d\u52a1\u67b6\u6784<\/h4>\n
\u540e\u7aef\u91c7\u7528\u5fae\u670d\u52a1\u67b6\u6784，\u4e3b\u8981\u5305\u542b\u4ee5\u4e0b\u670d\u52a1：<\/p>\n
- API\u7f51\u5173\u670d\u52a1：\u8d1f\u8d23\u8bf7\u6c42\u8def\u7531\u3001\u9274\u6743\u3001\u9650\u6d41<\/li>\n
- \u94fe\u63a5\u89e3\u6790\u670d\u52a1：\u8bc6\u522b\u5e73\u53f0\u7c7b\u578b，\u63d0\u53d6\u539f\u59cb\u89c6\u9891\/\u56fe\u7247\u5730\u5740<\/li>\n
- AI\u53bb\u6c34\u5370\u670d\u52a1：\u6838\u5fc3\u8ba1\u7b97\u670d\u52a1，\u90e8\u7f72\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/li>\n
- \u4efb\u52a1\u961f\u5217\u670d\u52a1：\u57fa\u4e8eRedis\u7684\u5f02\u6b65\u4efb\u52a1\u961f\u5217，\u524a\u5cf0\u586b\u8c37<\/li>\n
- \u6587\u4ef6\u5b58\u50a8\u670d\u52a1：\u5904\u7406\u7ed3\u679c\u7684\u4e34\u65f6\u5b58\u50a8 + CDN\u52a0\u901f\u5206\u53d1<\/li>\n
\u6280\u672f\u6808\u9009\u62e9：<\/p>\n
- \n
- \u670d\u52a1\u6846\u67b6：FastAPI（Python）+ Gin（Go）\u6df7\u5408\u90e8\u7f72<\/li>\n
- \u6a21\u578b\u63a8\u7406：PyTorch + ONNX Runtime + TensorRT\u52a0\u901f<\/li>\n
- \u6d88\u606f\u961f\u5217：Redis + Celery<\/li>\n
- \u5b58\u50a8：MinIO\u5bf9\u8c61\u5b58\u50a8 + \u963f\u91cc\u4e91CDN<\/li>\n<\/ul>\n
\n\u56db\u3001\u6838\u5fc3\u529f\u80fd\u6a21\u5757\u6280\u672f\u5b9e\u73b0<\/h3>\n
4.1 \u591a\u5e73\u53f0\u94fe\u63a5\u89e3\u6790\u6a21\u5757<\/h4>\n
\u94fe\u63a5\u89e3\u6790\u662f\u6574\u4e2a\u6d41\u7a0b\u7684\u7b2c\u4e00\u6b65，\u4e5f\u662f\u7528\u6237\u4f53\u9a8c\u7684\u7b2c\u4e00\u9053\u5173\u5361\u3002\u6211\u4eec\u652f\u6301100+\u4e3b\u6d41\u5185\u5bb9\u5e73\u53f0\u7684\u89c6\u9891\u548c\u56fe\u7247\u89e3\u6790，\u6838\u5fc3\u6280\u672f\u601d\u8def\u5982\u4e0b：<\/p>\n
4.1.1 \u5e73\u53f0\u8bc6\u522b<\/h5>\n
\u901a\u8fc7\u6b63\u5219\u8868\u8fbe\u5f0f\u5339\u914dURL\u7684\u57df\u540d\u7279\u5f81，\u5feb\u901f\u8bc6\u522b\u7528\u6237\u7c98\u8d34\u7684\u94fe\u63a5\u6765\u81ea\u54ea\u4e2a\u5e73\u53f0：<\/p>\n
PLATFORM_PATTERNS = {
\n "douyin": [
\n r"https?:\/\/(?:www\\\\.)?douyin\\\\.com\/video\/(\\\\d+)",
\n r"https?:\/\/v\\\\.douyin\\\\.com\/(\\\\w+)\/?",
\n ],
\n "xiaohongshu": [
\n r"https?:\/\/(?:www\\\\.)?xiaohongshu\\\\.com\/explore\/(\\\\w+)",
\n r"https?:\/\/xhslink\\\\.com\/(\\\\w+)",
\n ],
\n "kuaishou": [
\n r"https?:\/\/(?:www\\\\.)?kuaishou\\\\.com\/short-video\/(\\\\w+)",
\n r"https?:\/\/v\\\\.kuaishou\\\\.com\/(\\\\w+)",
\n ],
\n}<\/p>\n4.1.2 \u539f\u59cb\u8d44\u6e90\u63d0\u53d6<\/h5>\n
\u4e0d\u540c\u5e73\u53f0\u7684\u89c6\u9891\/\u56fe\u7247\u5730\u5740\u83b7\u53d6\u65b9\u5f0f\u5dee\u5f02\u5f88\u5927，\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\u7c7b\u578b：<\/p>\n
- \u516c\u5f00API\u578b：\u90e8\u5206\u5e73\u53f0\u6709\u516c\u5f00\u7684\u5206\u4eab\u9875\u9762，\u901a\u8fc7HTTP\u8bf7\u6c42\u89e3\u6790HTML\u4e2d\u7684meta\u6807\u7b7e\u5373\u53ef\u83b7\u53d6\u539f\u59cb\u89c6\u9891\u5730\u5740<\/li>\n
- \u63a5\u53e3\u8bf7\u6c42\u578b：\u9700\u8981\u6a21\u62dfApp\u7aef\u7684API\u8bf7\u6c42，\u643a\u5e26\u7279\u5b9a\u53c2\u6570\u548c\u7b7e\u540d\u83b7\u53d6\u6570\u636e<\/li>\n
- \u91cd\u5b9a\u5411\u578b：\u77ed\u94fe\u63a5\u9700\u8981\u7ecf\u8fc7\u591a\u6b21\u91cd\u5b9a\u5411\u624d\u80fd\u5230\u8fbe\u6700\u7ec8\u9875\u9762<\/li>\n
- \u52a0\u5bc6\u578b：\u90e8\u5206\u5e73\u53f0\u5bf9\u89c6\u9891\u5730\u5740\u505a\u4e86\u52a0\u5bc6\u5904\u7406，\u9700\u8981\u89e3\u5bc6\u7b97\u6cd5<\/li>\n
\u9488\u5bf9\u4e0d\u540c\u5e73\u53f0，\u6211\u4eec\u7f16\u5199\u4e86\u5bf9\u5e94\u7684\u89e3\u6790\u9002\u914d\u5668（Adapter Pattern），\u4fdd\u8bc1\u4e86\u7cfb\u7edf\u7684\u53ef\u6269\u5c55\u6027\u3002<\/p>\n
4.2 AI\u53bb\u6c34\u5370\u5f15\u64ce<\/h4>\n
\u8fd9\u662f\u6574\u4e2a\u7cfb\u7edf\u7684\u6838\u5fc3\u3002\u6211\u4eec\u91c7\u7528\u4e86**"\u591a\u6a21\u578b\u878d\u5408"**\u7684\u7b56\u7565，\u9488\u5bf9\u4e0d\u540c\u573a\u666f\u9009\u62e9\u6700\u4f18\u6a21\u578b：<\/p>\n
4.2.1 \u6a21\u578b\u9009\u62e9\u4e0e\u4f18\u5316<\/h5>\n
\u7ecf\u8fc7\u5927\u91cf\u5b9e\u9a8c\u5bf9\u6bd4，\u6211\u4eec\u6700\u7ec8\u9009\u7528\u4e86\u4ee5\u4e0b\u6a21\u578b\u7ec4\u5408：<\/p>\n
\n
\u573a\u666f\u9009\u7528\u6a21\u578b\u7279\u70b9<\/tr>\n \n\n \u5c0f\u9762\u79ef\u6c34\u5370（<5%\u753b\u9762）<\/td>\n LaMa-small<\/td>\n \u901f\u5ea6\u5feb\u3001\u8d28\u91cf\u597d<\/td>\n<\/tr>\n \n \u5927\u9762\u79ef\u6c34\u5370\/\u590d\u6742\u80cc\u666f<\/td>\n Stable Diffusion Inpainting<\/td>\n \u751f\u6210\u8d28\u91cf\u6700\u9ad8<\/td>\n<\/tr>\n \n \u89c6\u9891\u53bb\u6c34\u5370<\/td>\n ProPainter + \u5149\u6d41\u4e00\u81f4\u6027\u7ea6\u675f<\/td>\n \u65f6\u5e8f\u4e00\u81f4\u6027\u597d<\/td>\n<\/tr>\n \n \u5b9e\u65f6\u9884\u89c8\u6a21\u5f0f<\/td>\n \u5feb\u901f\u8fb9\u7f18\u4fee\u590d\u7b97\u6cd5<\/td>\n \u6beb\u79d2\u7ea7\u54cd\u5e94<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u4e3a\u4e86\u5728\u670d\u52a1\u7aef\u9ad8\u6548\u90e8\u7f72，\u6211\u4eec\u5bf9\u6a21\u578b\u505a\u4e86\u4ee5\u4e0b\u4f18\u5316：<\/p>\n
- ONNX\u5bfc\u51fa + TensorRT\u52a0\u901f：\u5c06PyTorch\u6a21\u578b\u5bfc\u51fa\u4e3aONNX\u683c\u5f0f，\u518d\u901a\u8fc7TensorRT\u8fdb\u884cFP16\u91cf\u5316\u548c\u7b97\u5b50\u878d\u5408，\u63a8\u7406\u901f\u5ea6\u63d0\u53473-5\u500d<\/li>\n
- \u52a8\u6001\u6279\u5904\u7406（Dynamic Batching）：\u5c06\u77ed\u65f6\u95f4\u5185\u7684\u591a\u4e2a\u8bf7\u6c42\u5408\u5e76\u6210\u4e00\u4e2abatch\u5904\u7406，\u63d0\u9ad8GPU\u5229\u7528\u7387<\/li>\n
- \u6a21\u578b\u9884\u70ed + \u5e38\u9a7b\u5185\u5b58：\u907f\u514d\u9996\u6b21\u8bf7\u6c42\u7684\u51b7\u542f\u52a8\u5ef6\u8fdf<\/li>\n
- \u591a\u7ea7\u7f13\u5b58：\u76f8\u540c\u8f93\u5165\u7684\u7ed3\u679c\u7f13\u5b58，\u51cf\u5c11\u91cd\u590d\u8ba1\u7b97<\/li>\n
4.2.2 \u6c34\u5370\u68c0\u6d4b\u4e0e\u5b9a\u4f4d<\/h5>\n
\u53bb\u6c34\u5370\u7684\u7b2c\u4e00\u6b65\u662f\u8981\u77e5\u9053\u6c34\u5370\u5728\u54ea\u91cc\u3002\u6211\u4eec\u91c7\u7528\u4e86**"\u5148\u68c0\u6d4b\u3001\u540e\u4fee\u590d"**\u7684\u4e24\u9636\u6bb5\u7b56\u7565：<\/p>\n
\u6c34\u5370\u68c0\u6d4b\u9636\u6bb5：<\/p>\n
- \n
- \u5bf9\u4e8e\u5df2\u77e5\u5e73\u53f0\u7684\u56fa\u5b9a\u4f4d\u7f6e\u6c34\u5370（\u5982\u5e73\u53f0logo），\u76f4\u63a5\u4f7f\u7528\u9884\u8bbe\u4f4d\u7f6e\u63a9\u7801<\/li>\n
- \u5bf9\u4e8e\u4f4d\u7f6e\u4e0d\u56fa\u5b9a\u7684\u6c34\u5370（\u5982\u7528\u6237ID\u3001\u6587\u5b57\u6c34\u5370），\u4f7f\u7528\u76ee\u6807\u68c0\u6d4b\u6a21\u578b（YOLO\u7cfb\u5217）\u8fdb\u884c\u68c0\u6d4b<\/li>\n
- \u5bf9\u4e8e\u7eaf\u8272\u6587\u5b57\u6c34\u5370，\u8fd8\u53ef\u4ee5\u901a\u8fc7\u989c\u8272\u9608\u503c + \u5f62\u6001\u5b66\u64cd\u4f5c\u5feb\u901f\u5b9a\u4f4d<\/li>\n<\/ul>\n
\u63a9\u7801\u751f\u6210\u9636\u6bb5：<\/p>\n
- \n
- \u68c0\u6d4b\u51fa\u6c34\u5370\u533a\u57df\u540e，\u751f\u6210\u5bf9\u5e94\u7684\u4e8c\u503c\u63a9\u7801（mask）<\/li>\n
- \u5bf9\u63a9\u7801\u8fb9\u7f18\u505a\u9002\u5f53\u7684\u7fbd\u5316（feather）\u5904\u7406，\u907f\u514d\u4fee\u590d\u8fb9\u7f18\u51fa\u73b0\u786c\u8fb9<\/li>\n
- \u63a9\u7801\u533a\u57df\u9002\u5f53\u5916\u6269（dilate），\u786e\u4fdd\u6c34\u5370\u5b8c\u5168\u88ab\u8986\u76d6<\/li>\n<\/ul>\n
4.3 \u89c6\u9891\u53bb\u6c34\u5370\u7684\u5de5\u7a0b\u4f18\u5316<\/h4>\n
\u89c6\u9891\u53bb\u6c34\u5370\u8ba1\u7b97\u91cf\u5927，\u4e3a\u4e86\u4fdd\u8bc1\u7528\u6237\u4f53\u9a8c，\u6211\u4eec\u505a\u4e86\u4ee5\u4e0b\u5de5\u7a0b\u4f18\u5316：<\/p>\n
4.3.1 \u5173\u952e\u5e27\u7b56\u7565<\/h5>\n
\u5e76\u975e\u6bcf\u4e00\u5e27\u90fd\u9700\u8981AI\u4fee\u590d\u3002\u5bf9\u4e8e\u6c34\u5370\u4f4d\u7f6e\u56fa\u5b9a\u3001\u80cc\u666f\u53d8\u5316\u4e0d\u5927\u7684\u89c6\u9891，\u53ef\u4ee5\u91c7\u7528：<\/p>\n
- \n
- \u6bcfN\u5e27\u9009\u4e00\u4e2a\u5173\u952e\u5e27\u505aAI\u4fee\u590d<\/li>\n
- \u4e2d\u95f4\u5e27\u901a\u8fc7\u5149\u6d41\u63d2\u503c（warping）\u4ece\u76f8\u90bb\u5173\u952e\u5e27\u83b7\u53d6\u4fee\u590d\u4fe1\u606f<\/li>\n
- \u68c0\u6d4b\u5230\u573a\u666f\u5207\u6362\u65f6\u5f3a\u5236\u751f\u6210\u65b0\u7684\u5173\u952e\u5e27<\/li>\n<\/ul>\n
\u8fd9\u6837\u53ef\u4ee5\u5c06\u8ba1\u7b97\u91cf\u964d\u4f4e\u5230\u539f\u6765\u76841\/5\u52301\/10\u3002<\/p>\n
4.3.2 \u5206\u5757\u5904\u7406<\/h5>\n
\u5bf9\u4e8e\u9ad8\u5206\u8fa8\u7387\u89c6\u9891（\u59821080p\u30014K），\u76f4\u63a5\u9001\u5165\u6a21\u578b\u4f1a\u8d85\u51fa\u663e\u5b58\u9650\u5236\u3002\u6211\u4eec\u91c7\u7528\u5206\u5757\u5904\u7406\u7b56\u7565：<\/p>\n
- \n
- \u5c06\u89c6\u9891\u5e27\u5207\u5206\u4e3a\u591a\u4e2a\u91cd\u53e0\u7684\u5c0f\u5757（patch）<\/li>\n
- \u9010\u5757\u9001\u5165\u6a21\u578b\u5904\u7406<\/li>\n
- \u6700\u540e\u901a\u8fc7\u52a0\u6743\u878d\u5408\u7684\u65b9\u5f0f\u62fc\u63a5\u56de\u539f\u56fe，\u907f\u514d\u5757\u6548\u5e94<\/li>\n<\/ul>\n
4.3.3 \u5f02\u6b65\u5904\u7406 + \u8fdb\u5ea6\u63a8\u9001<\/h5>\n
\u8003\u8651\u5230\u89c6\u9891\u5904\u7406\u53ef\u80fd\u9700\u8981\u51e0\u5341\u79d2\u751a\u81f3\u66f4\u957f\u65f6\u95f4，\u6211\u4eec\u91c7\u7528\u4e86\u5f02\u6b65\u5904\u7406\u6a21\u5f0f：<\/p>\n
- \u7528\u6237\u63d0\u4ea4\u4efb\u52a1\u540e\u7acb\u5373\u8fd4\u56de\u4efb\u52a1ID<\/li>\n
- \u540e\u7aef\u901a\u8fc7\u4efb\u52a1\u961f\u5217\u5f02\u6b65\u5904\u7406<\/li>\n
- \u5c0f\u7a0b\u5e8f\u7aef\u901a\u8fc7WebSocket\u8f6e\u8be2\u4efb\u52a1\u8fdb\u5ea6<\/li>\n
- \u5904\u7406\u5b8c\u6210\u540e\u63a8\u9001\u901a\u77e5\u7ed9\u7528\u6237<\/li>\n
\n\u4e94\u3001\u5173\u952e\u6280\u672f\u96be\u70b9\u4e0e\u89e3\u51b3\u65b9\u6848<\/h3>\n
5.1 \u753b\u8d28\u4fdd\u6301\u95ee\u9898<\/h4>\n
\u95ee\u9898\u63cf\u8ff0：\u5f88\u591a\u53bb\u6c34\u5370\u5de5\u5177\u5904\u7406\u540e\u753b\u8d28\u660e\u663e\u4e0b\u964d，\u51fa\u73b0\u6a21\u7cca\u3001\u8272\u5757\u7b49\u95ee\u9898\u3002<\/p>\n
\u89e3\u51b3\u65b9\u6848：<\/p>\n
- \u91c7\u7528\u539f\u56fe\u5206\u8fa8\u7387\u5904\u7406：\u4e0d\u964d\u91c7\u6837，\u4fdd\u8bc1\u8f93\u51fa\u6e05\u6670\u5ea6<\/li>\n
- \u4f7f\u7528\u611f\u77e5\u635f\u5931（Perceptual Loss）：\u8bad\u7ec3\u65f6\u52a0\u5165VGG\u7279\u5f81\u635f\u5931，\u4fdd\u8bc1\u89c6\u89c9\u8d28\u91cf<\/li>\n
- \u540e\u5904\u7406\u589e\u5f3a：\u4fee\u590d\u5b8c\u6210\u540e\u505a\u8f7b\u5fae\u7684\u9510\u5316\u548c\u8272\u5f69\u6821\u6b63<\/li>\n
- \u8d28\u91cf\u8bc4\u4f30\u673a\u5236：\u81ea\u52a8\u8bc4\u4f30\u4fee\u590d\u8d28\u91cf，\u4e0d\u5408\u683c\u7684\u81ea\u52a8\u5207\u6362\u66f4\u597d\u7684\u6a21\u578b\u91cd\u8bd5<\/li>\n
5.2 \u5904\u7406\u901f\u5ea6\u4f18\u5316<\/h4>\n
\u95ee\u9898\u63cf\u8ff0：AI\u6a21\u578b\u63a8\u7406\u8ba1\u7b97\u91cf\u5927，\u7528\u6237\u7b49\u5f85\u65f6\u95f4\u957f\u3002<\/p>\n
\u89e3\u51b3\u65b9\u6848：<\/p>\n
- \u6a21\u578b\u8f7b\u91cf\u5316：\u4f7f\u7528\u77e5\u8bc6\u84b8\u998f（Knowledge Distillation）\u5c06\u5927\u6a21\u578b\u538b\u7f29\u4e3a\u5c0f\u6a21\u578b<\/li>\n
- \u591a\u6a21\u578b\u5206\u7ea7：\u7b80\u5355\u573a\u666f\u7528\u5feb\u901f\u6a21\u578b，\u590d\u6742\u573a\u666f\u7528\u9ad8\u8d28\u91cf\u6a21\u578b<\/li>\n
- \u7b97\u529b\u5f39\u6027\u4f38\u7f29：\u57fa\u4e8eK8s\u7684\u81ea\u52a8\u6269\u7f29\u5bb9，\u9ad8\u5cf0\u671f\u81ea\u52a8\u589e\u52a0GPU\u8282\u70b9<\/li>\n
- \u9884\u89e3\u6790\u7f13\u5b58：\u70ed\u95e8\u89c6\u9891\u7684\u89e3\u6790\u7ed3\u679c\u7f13\u5b58，\u51cf\u5c11\u91cd\u590d\u8ba1\u7b97<\/li>\n
5.3 \u591a\u5e73\u53f0\u517c\u5bb9\u6027<\/h4>\n
\u95ee\u9898\u63cf\u8ff0：\u4e0d\u540c\u5e73\u53f0\u7684\u89c6\u9891\u683c\u5f0f\u3001\u7f16\u7801\u65b9\u5f0f\u3001\u6c34\u5370\u4f4d\u7f6e\u5343\u5dee\u4e07\u522b\u3002<\/p>\n
\u89e3\u51b3\u65b9\u6848：<\/p>\n
- \u9002\u914d\u5668\u6a21\u5f0f：\u6bcf\u4e2a\u5e73\u53f0\u4e00\u4e2a\u89e3\u6790\u9002\u914d\u5668，\u65b0\u589e\u5e73\u53f0\u53ea\u9700\u6dfb\u52a0\u65b0\u9002\u914d\u5668<\/li>\n
- \u7edf\u4e00\u4e2d\u95f4\u683c\u5f0f：\u6240\u6709\u5e73\u53f0\u7684\u89e3\u6790\u7ed3\u679c\u7edf\u4e00\u8f6c\u6362\u4e3a\u5185\u90e8\u6807\u51c6\u683c\u5f0f<\/li>\n
- \u5bb9\u9519\u964d\u7ea7\u673a\u5236：\u67d0\u4e2a\u5e73\u53f0\u89e3\u6790\u5931\u8d25\u65f6\u81ea\u52a8\u5c1d\u8bd5\u5907\u7528\u65b9\u6848<\/li>\n
5.4 \u5c0f\u7a0b\u5e8f\u7aef\u4f53\u9a8c\u4f18\u5316<\/h4>\n
\u95ee\u9898\u63cf\u8ff0：\u5c0f\u7a0b\u5e8f\u6027\u80fd\u6709\u9650，\u5927\u6587\u4ef6\u9884\u89c8\u548c\u4fdd\u5b58\u5bb9\u6613\u5361\u987f\u3002<\/p>\n
\u89e3\u51b3\u65b9\u6848：<\/p>\n
- \u6d41\u5f0f\u52a0\u8f7d：\u89c6\u9891\u91c7\u7528\u6d41\u5f0f\u64ad\u653e，\u8fb9\u4e0b\u8fb9\u64ad<\/li>\n
- \u7f29\u7565\u56fe\u9884\u89c8：\u5148\u5c55\u793a\u4f4e\u6e05\u7f29\u7565\u56fe，\u9ad8\u6e05\u56fe\u61d2\u52a0\u8f7d<\/li>\n
- \u5206\u7247\u4e0b\u8f7d：\u5927\u6587\u4ef6\u5206\u7247\u4e0b\u8f7d，\u652f\u6301\u65ad\u70b9\u7eed\u4f20<\/li>\n
- \u5185\u5b58\u7ba1\u7406：\u53ca\u65f6\u91ca\u653e\u4e0d\u518d\u4f7f\u7528\u7684\u8d44\u6e90，\u907f\u514d\u5c0f\u7a0b\u5e8f\u88ab\u7cfb\u7edf\u56de\u6536<\/li>\n
\n\u516d\u3001\u6548\u679c\u5c55\u793a\u4e0e\u8bc4\u4f30<\/h3>\n
6.1 \u5b9a\u91cf\u8bc4\u4f30<\/h4>\n
\u6211\u4eec\u5728\u516c\u5f00\u6570\u636e\u96c6\u548c\u81ea\u5efa\u6d4b\u8bd5\u96c6\u4e0a\u5bf9\u7cfb\u7edf\u8fdb\u884c\u4e86\u5b9a\u91cf\u8bc4\u4f30，\u4e3b\u8981\u6307\u6807\u5305\u62ec：<\/p>\n
\n
\u6307\u6807\u8bf4\u660e\u6d4b\u8bd5\u7ed3\u679c<\/tr>\n \n\n PSNR（\u5cf0\u503c\u4fe1\u566a\u6bd4）<\/td>\n \u8861\u91cf\u50cf\u7d20\u7ea7\u76f8\u4f3c\u5ea6，\u8d8a\u9ad8\u8d8a\u597d<\/td>\n 32.8 dB<\/td>\n<\/tr>\n \n SSIM（\u7ed3\u6784\u76f8\u4f3c\u6027）<\/td>\n \u8861\u91cf\u7ed3\u6784\u76f8\u4f3c\u6027，0-1，\u8d8a\u9ad8\u8d8a\u597d<\/td>\n 0.942<\/td>\n<\/tr>\n \n LPIPS（\u611f\u77e5\u8ddd\u79bb）<\/td>\n \u8861\u91cf\u611f\u77e5\u8d28\u91cf，\u8d8a\u4f4e\u8d8a\u597d<\/td>\n 0.031<\/td>\n<\/tr>\n \n \u5e73\u5747\u5904\u7406\u65f6\u95f4（\u56fe\u7247）<\/td>\n 720p\u56fe\u7247\u5e73\u5747\u8017\u65f6<\/td>\n 1.2\u79d2<\/td>\n<\/tr>\n \n \u5e73\u5747\u5904\u7406\u65f6\u95f4（\u89c6\u9891）<\/td>\n 15\u79d2\u77ed\u89c6\u9891\u5e73\u5747\u8017\u65f6<\/td>\n 8\u79d2<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n 6.2 \u5b9a\u6027\u6548\u679c<\/h4>\n
\u4e3a\u4e86\u76f4\u89c2\u5c55\u793aAI\u53bb\u6c34\u5370\u7684\u5b9e\u9645\u6548\u679c，\u6211\u4eec\u9009\u53d6\u4e86\u51e0\u4e2a\u5178\u578b\u573a\u666f\u8fdb\u884c\u6d4b\u8bd5\u3002<\/p>\n
6.2.1 \u5e73\u53f0\u89c6\u9891\u6c34\u5370\u53bb\u9664<\/h5>\n
\u8fd9\u662f\u6700\u5e38\u89c1\u7684\u4f7f\u7528\u573a\u666f\u2014\u2014\u7528\u6237\u4ece\u77ed\u89c6\u9891\u5e73\u53f0\u4e0b\u8f7d\u7684\u89c6\u9891\u6216\u5c01\u9762\u56fe\u5e26\u6709\u5e73\u53f0logo\u548c\u521b\u4f5c\u8005ID\u6c34\u5370\u3002AI\u4fee\u590d\u540e，\u6c34\u5370\u88ab\u5b8c\u5168\u53bb\u9664，\u80cc\u666f\u7684\u53d1\u4e1d\u7ec6\u8282\u548c\u4eba\u7269\u8f6e\u5ed3\u90fd\u5f97\u5230\u4e86\u5f88\u597d\u7684\u8fd8\u539f，\u6ca1\u6709\u51fa\u73b0\u6a21\u7cca\u6216\u53d8\u5f62\u7684\u60c5\u51b5\u3002\u7531\u4e8eLaMa\u6a21\u578b\u7684\u5085\u91cc\u53f6\u5377\u79ef\u80fd\u591f\u6709\u6548\u6355\u6349\u5168\u5c40\u7eb9\u7406\u4fe1\u606f，\u5373\u4f7f\u662f\u53d1\u4e1d\u8fd9\u79cd\u7cbe\u7ec6\u7ed3\u6784\u4e5f\u80fd\u4fdd\u6301\u81ea\u7136\u8fde\u8d2f\u3002<\/p>\n
\n\n
\n \u53bb\u6c34\u5370\u524d（\u539f\u56fe）<\/p>\n
\u53bb\u6c34\u5370\u540e（AI\u4fee\u590d）<\/p>\n<\/tr>\n
\n ![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261007111747-6ac62a5be627f.png\")
<\/td>\n![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261007111748-6ac62a5c1880d.png\")
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n6.2.2 \u5927\u9762\u79ef\u6563\u70b9\u6c34\u5370\u53bb\u9664<\/h5>\n
\u5bf9\u4e8e\u904d\u5e03\u5168\u56fe\u7684\u6563\u70b9\u5f0f\u6c34\u5370（\u5982\u67d0\u4e9b\u5185\u5bb9\u5e73\u53f0\u7684\u9632\u76d7\u6c34\u5370），\u4f20\u7edf\u65b9\u6cd5\u51e0\u4e4e\u65e0\u80fd\u4e3a\u529b，\u53ea\u80fd\u505a\u6a21\u7cca\u5904\u7406\u3002\u800c\u57fa\u4e8e\u6269\u6563\u6a21\u578b\u7684AI\u4fee\u590d\u65b9\u6cd5\u80fd\u591f\u5f88\u597d\u5730\u5e94\u5bf9\u8fd9\u79cd\u6311\u6218\u3002\u5c3d\u7ba1\u6c34\u5370\u8986\u76d6\u4e86\u4eba\u7269\u9762\u90e8\u3001\u5934\u53d1\u3001\u8863\u670d\u7b49\u591a\u4e2a\u91cd\u8981\u533a\u57df，AI\u4ecd\u7136\u80fd\u591f\u51c6\u786e\u5730"\u7406\u89e3"\u56fe\u50cf\u5185\u5bb9\u5e76\u751f\u6210\u5408\u7406\u7684\u4fee\u590d\u7ed3\u679c\u3002\u9762\u90e8\u4e94\u5b98\u4fdd\u6301\u5bf9\u79f0\u81ea\u7136，\u5934\u53d1\u4e1d\u7f15\u5206\u660e，\u8863\u670d\u7eb9\u7406\u548c\u80cc\u666f\u7ed3\u6784\u4e5f\u6ca1\u6709\u660e\u663e\u7684\u4fee\u590d\u75d5\u8ff9\u3002\u8fd9\u5f97\u76ca\u4e8eStable Diffusion Inpainting\u6a21\u578b\u5f3a\u5927\u7684\u8bed\u4e49\u7406\u89e3\u80fd\u529b\u548c\u751f\u6210\u80fd\u529b\u3002
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261007111748-6ac62a5c3d993.jpg\")
<\/p>\n6.2.3 AI\u751f\u6210\u5185\u5bb9\u6c34\u5370\u53bb\u9664<\/h5>\n
\u968f\u7740AIGC\u7684\u5174\u8d77，\u5f88\u591aAI\u751f\u6210\u7684\u56fe\u7247\u4e5f\u4f1a\u5e26\u6709\u5e73\u53f0\u6c34\u5370\u6216\u751f\u6210\u6807\u8bc6\u3002\u8fd9\u7c7b\u6c34\u5370\u901a\u5e38\u4f4d\u4e8e\u753b\u9762\u8fb9\u7f18，\u4f46\u80cc\u666f\u53ef\u80fd\u6bd4\u8f83\u590d\u6742\u3002\u7ecf\u8fc7AI\u5904\u7406\u540e，\u6c34\u5370\u88ab\u5e72\u51c0\u5229\u843d\u5730\u53bb\u9664，\u80cc\u666f\u7684\u8863\u670d\u548c\u76ae\u80a4\u8fc7\u6e21\u81ea\u7136，\u6ca1\u6709\u4efb\u4f55PS\u75d5\u8ff9\u3002
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261007111748-6ac62a5c940b0.jpg\")
<\/p>\n6.2.4 \u6548\u679c\u603b\u7ed3<\/h5>\n
\u4ece\u4ee5\u4e0a\u591a\u4e2a\u573a\u666f\u7684\u6d4b\u8bd5\u6548\u679c\u6765\u770b：<\/p>\n
- \n
- \u5c0f\u9762\u79ef\u6c34\u5370：\u51e0\u4e4e\u65e0\u75d5\u4fee\u590d，\u8089\u773c\u96be\u4ee5\u5bdf\u89c9<\/li>\n
- \u4e2d\u7b49\u9762\u79ef\u6c34\u5370：\u5927\u90e8\u5206\u573a\u666f\u4fee\u590d\u81ea\u7136，\u7eb9\u7406\u8fde\u8d2f<\/li>\n
- \u5927\u9762\u79ef\u590d\u6742\u6c34\u5370：\u5728\u7b80\u5355\u80cc\u666f\u4e0b\u6548\u679c\u826f\u597d，\u590d\u6742\u573a\u666f\u4e0b\u53ef\u80fd\u6709\u8f7b\u5fae\u7455\u75b5<\/li>\n
- \u89c6\u9891\u53bb\u6c34\u5370：\u65f6\u5e8f\u4e00\u81f4\u6027\u826f\u597d，\u65e0\u660e\u663e\u95ea\u70c1\u6296\u52a8<\/li>\n<\/ul>\n
\n\u4e03\u3001\u603b\u7ed3\u4e0e\u5c55\u671b<\/h3>\n
7.1 \u603b\u7ed3<\/h4>\n
\u672c\u6587\u4ece\u6280\u672f\u89d2\u5ea6\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u4e00\u4e2aAI\u53bb\u6c34\u5370\u5fae\u4fe1\u5c0f\u7a0b\u5e8f\u7684\u5b8c\u6574\u5b9e\u73b0\u65b9\u6848，\u6db5\u76d6\u4e86：<\/p>\n
- \n
- AI\u53bb\u6c34\u5370\u7684\u6838\u5fc3\u6280\u672f\u539f\u7406（GAN\u3001\u6269\u6563\u6a21\u578b\u3001\u89c6\u9891\u4fee\u590d）<\/li>\n
- \u524d\u540e\u7aef\u5206\u79bb\u7684\u7cfb\u7edf\u67b6\u6784\u8bbe\u8ba1<\/li>\n
- \u591a\u5e73\u53f0\u94fe\u63a5\u89e3\u6790\u3001AI\u53bb\u6c34\u5370\u5f15\u64ce\u3001\u89c6\u9891\u4f18\u5316\u7b49\u6838\u5fc3\u6a21\u5757\u7684\u5b9e\u73b0\u7ec6\u8282<\/li>\n
- \u753b\u8d28\u4fdd\u6301\u3001\u901f\u5ea6\u4f18\u5316\u3001\u517c\u5bb9\u6027\u7b49\u5173\u952e\u96be\u70b9\u7684\u89e3\u51b3\u65b9\u6848<\/li>\n<\/ul>\n
\u300c\u730e\u624b\u53bb\u6c34\u5370\u300d\u5c0f\u7a0b\u5e8f\u4f5c\u4e3a\u4e00\u4e2a\u6280\u672f\u5b9e\u8df5\u6848\u4f8b，\u9a8c\u8bc1\u4e86\u5c06\u524d\u6cbf\u7684\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u4e0e\u8f7b\u91cf\u7ea7\u7684\u5fae\u4fe1\u5c0f\u7a0b\u5e8f\u76f8\u7ed3\u5408\u7684\u53ef\u884c\u6027，\u4e3a\u540c\u7c7b\u5de5\u5177\u7684\u5f00\u53d1\u63d0\u4f9b\u4e86\u53c2\u8003\u3002<\/p>\n
7.2 \u672a\u6765\u5c55\u671b<\/h4>\n
\u968f\u7740AI\u6280\u672f\u7684\u4e0d\u65ad\u8fdb\u6b65，\u6211\u4eec\u8ba1\u5212\u5728\u4ee5\u4e0b\u65b9\u5411\u7ee7\u7eed\u4f18\u5316：<\/p>\n
- \u7aef\u4fa7AI\u63a8\u7406：\u63a2\u7d22\u5c06\u8f7b\u91cf\u7ea7\u6a21\u578b\u90e8\u7f72\u5230\u5c0f\u7a0b\u5e8f\u7aef，\u5b9e\u73b0\u79bb\u7ebf\u53bb\u6c34\u5370<\/li>\n
- \u89c6\u9891\u5b9e\u65f6\u53bb\u6c34\u5370：\u7ed3\u5408WebAssembly\u548cWebGPU，\u5728\u6d4f\u89c8\u5668\u7aef\u5b9e\u73b0\u5b9e\u65f6\u5904\u7406<\/li>\n
- \u8bed\u4e49\u7406\u89e3\u589e\u5f3a：\u5229\u7528\u5927\u6a21\u578b\u7406\u89e3\u56fe\u50cf\u8bed\u4e49，\u5f15\u5bfc\u66f4\u667a\u80fd\u7684\u4fee\u590d\u65b9\u5411<\/li>\n
- \u66f4\u591a\u521b\u610f\u529f\u80fd：\u5982AI\u6269\u56fe\u3001\u98ce\u683c\u8fc1\u79fb\u3001\u753b\u8d28\u589e\u5f3a\u7b49\u5468\u8fb9\u529f\u80fd<\/li>\n
\u6280\u672f\u7684\u4ef7\u503c\u5728\u4e8e\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002\u5e0c\u671b\u672c\u6587\u7684\u5206\u4eab\u80fd\u591f\u5bf9\u4ece\u4e8b\u76f8\u5173\u5f00\u53d1\u7684\u540c\u5b66\u6709\u6240\u5e2e\u52a9，\u4e5f\u6b22\u8fce\u5927\u5bb6\u4ea4\u6d41\u8ba8\u8bba，\u5171\u540c\u63a8\u52a8\u6280\u672f\u7684\u8fdb\u6b65\u3002<\/p>\n
\u6b22\u8fce\u5927\u5bb6\u8bbf\u95ee\u300c\u730e\u624b\u53bb\u6c34\u5370\u300d<\/span>\u5c0f\u7a0b\u5e8f，\u591a\u63d0\u5b9d\u8d35\u610f\u89c1！<\/p>\n
\n\u53c2\u8003\u8d44\u6599：<\/p>\n
- \n
- Yu J, Lin Z, Yang J, et al. Free-Form Image Inpainting with Gated Convolution[C]\/\/ICCV, 2019.<\/li>\n
- Liu G, Reda F A, Shih K J, et al. Partial Convolution based Padding[J]. arXiv, 2018.<\/li>\n
- Suvorov R, Logacheva E, Mashikhin A, et al. Resolution-robust Large Mask Inpainting with Fourier Convolutions[C]\/\/WACV, 2022.<\/li>\n
- Zhou S, Xu L, Liu J, et al. ProPainter: Improving Propagation and Transformer for Video Inpainting[C]\/\/CVPR, 2023.<\/li>\n
- Rombach R, Blattmann A, Lorenz D, et al. High-Resolution Image Synthesis with Latent Diffusion Models[C]\/\/CVPR, 2022.<\/li>\n<\/ul>\n
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