![\"TryOffAnyone\uff1a\u4ece\u4eba\u7269\u8eab\u4e0a\u63d0\u53d6\u670d\u88c5\u4e3a\u5e73\u94fa\u670d\u88c5\u5c55\u793a\u56fe\u7684AI\u5de5\u5177-1\"](\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/01\/69d89f30ffd2614.png\" "\\"TryOffAnyone\uff1a\u4ece\u4eba\u7269\u8eab\u4e0a\u63d0\u53d6\u670d\u88c5\u4e3a\u5e73\u94fa\u670d\u88c5\u5c55\u793a\u56fe\u7684AI\u5de5\u5177-1\\"")
\u4f53\u9a8c\u5730\u5740\uff1ahttps:\/\/huggingface.co\/spaces\/1aurent\/TryOffAnyone<\/p><\/div>\n
<\/p>\n
![\"TryOffAnyone\uff1a\u4ece\u4eba\u7269\u8eab\u4e0a\u63d0\u53d6\u670d\u88c5\u4e3a\u5e73\u94fa\u670d\u88c5\u5c55\u793a\u56fe\u7684AI\u5de5\u5177-1\"](\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/01\/fd9da21c1d47bef.png\" "\\"TryOffAnyone\uff1a\u4ece\u4eba\u7269\u8eab\u4e0a\u63d0\u53d6\u670d\u88c5\u4e3a\u5e73\u94fa\u670d\u88c5\u5c55\u793a\u56fe\u7684AI\u5de5\u5177-1\\"")
<\/p>\n
<\/p>\n
\u529f\u80fd\u5217\u8868<\/h2>\n\n- \u652f\u6301\u5728\u7ebf\u56fe\u7247URL\u76f4\u63a5\u5904\u7406\u529f\u80fd<\/li>\n
- \u667a\u80fd\u8bc6\u522b\u5e76\u63d0\u53d6\u56fe\u50cf\u4e2d\u7684\u670d\u88c5\u533a\u57df<\/li>\n
- \u81ea\u52a8\u5c06\u7a7f\u7740\u72b6\u6001\u670d\u88c5\u8f6c\u6362\u4e3a\u5e73\u94fa\u6548\u679c<\/li>\n
- \u5185\u7f6e\u4e13\u4e1a\u7684\u80cc\u666f\u53bb\u9664\u548c\u56fe\u50cf\u4f18\u5316\u5904\u7406<\/li>\n
- \u652f\u6301VITON-HD\u6570\u636e\u96c6\u7684\u6279\u91cf\u6d4b\u8bd5\u529f\u80fd<\/li>\n
- \u63d0\u4f9b\u8be6\u7ec6\u7684\u6a21\u578b\u8bc4\u4f30\u6307\u6807\u8ba1\u7b97<\/li>\n
- \u96c6\u6210\u591a\u79cd\u56fe\u50cf\u8d28\u91cf\u8bc4\u4f30\u65b9\u6cd5(SSIM, LPIPS, FID, KID)<\/li>\n
- \u652f\u6301\u81ea\u5b9a\u4e49\u56fe\u50cf\u5c3a\u5bf8\u548c\u5904\u7406\u53c2\u6570<\/li>\n
- \u63d0\u4f9b\u9884\u8bad\u7ec3\u6a21\u578b\u5feb\u901f\u90e8\u7f72\u80fd\u529b<\/li>\n
- \u652f\u6301GPU\u52a0\u901f\u5904\u7406<\/li>\n<\/ul>\n
<\/p>\n
\u4f7f\u7528\u5e2e\u52a9<\/h2>\n1. \u73af\u5883\u914d\u7f6e\u4e0e\u5b89\u88c5<\/h3>\n
\u9996\u5148\u9700\u8981\u786e\u4fdd\u60a8\u7684\u7cfb\u7edf\u6ee1\u8db3\u4ee5\u4e0b\u8981\u6c42\uff1a<\/p>\n
\n- Python 3.x \u73af\u5883<\/li>\n
- CUDA\u652f\u6301\u7684GPU\uff08\u63a8\u8350\u7528\u4e8e\u52a0\u901f\u5904\u7406\uff09<\/li>\n
- Git\u7248\u672c\u63a7\u5236\u5de5\u5177<\/li>\n<\/ul>\n
\u5b89\u88c5\u6b65\u9aa4\uff1a<\/p>\n
# 1. \u514b\u9686\u9879\u76ee\u4ee3\u7801\r\ngit clone https:\/\/github.com\/ixarchakos\/try-off-anyone.git\r\ncd try-off-anyone\r\n# 2. \u5b89\u88c5\u4f9d\u8d56\u5305\r\npython3 -m pip install -r requirements.txt\r\n# 3. \u4e0b\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\r\n# \u8bbf\u95ee https:\/\/huggingface.co\/ixarchakos\/tryOffAnyone\r\n# \u5c06\u4e0b\u8f7d\u7684\u6a21\u578b\u6587\u4ef6\u653e\u7f6e\u5728 \"try-off-anyone\/ckpt\/\" \u76ee\u5f55\u4e0b\r\n<\/code><\/pre>\n2. \u57fa\u672c\u4f7f\u7528\u65b9\u6cd5<\/h3>\n2.1 \u5904\u7406\u5355\u5f20\u5728\u7ebf\u56fe\u7247<\/h4>\npython3 main.py --inference --url=\"\u60a8\u7684\u56fe\u7247URL\u5730\u5740\"\r\n<\/code><\/pre>\n\u5904\u7406\u540e\u7684\u56fe\u7247\u5c06\u4fdd\u5b58\u5728 “try-off-anyone\/data\/” \u76ee\u5f55\u4e0b\u3002<\/p>\n
2.2 \u53c2\u6570\u914d\u7f6e\u8bf4\u660e<\/h4>\n\n- –seed\uff1a\u968f\u673a\u79cd\u5b50\u8bbe\u7f6e\uff08\u9ed8\u8ba4\uff1a36\uff09<\/li>\n
- –steps\uff1a\u5904\u7406\u6b65\u9aa4\u6570\uff08\u9ed8\u8ba4\uff1a50\uff09<\/li>\n
- –scale\uff1a\u7f29\u653e\u6bd4\u4f8b\uff08\u9ed8\u8ba4\uff1a2.5\uff09<\/li>\n
- –width\uff1a\u8f93\u51fa\u56fe\u50cf\u5bbd\u5ea6\uff08\u9ed8\u8ba4\uff1a384\uff09<\/li>\n
- –height\uff1a\u8f93\u51fa\u56fe\u50cf\u9ad8\u5ea6\uff08\u9ed8\u8ba4\uff1a512\uff09<\/li>\n
- –gpu_id\uff1a\u6307\u5b9aGPU\u8bbe\u5907ID\uff08\u9ed8\u8ba4\uff1a0\uff09<\/li>\n<\/ul>\n
3. \u9ad8\u7ea7\u529f\u80fd\u4f7f\u7528<\/h3>\n3.1 \u6279\u91cf\u5904\u7406VITON-HD\u6570\u636e\u96c6<\/h4>\n\n- \u4e0b\u8f7dVITON-HD\u539f\u59cb\u6570\u636e\u96c6<\/li>\n
- \u4e0b\u8f7d\u670d\u88c5\u56fe\u50cf\u8499\u7248\u6587\u4ef6\uff1a\n
\n- \u8bbf\u95ee\uff1ahttps:\/\/huggingface.co\/ixarchakos\/tryOffAnyone\/blob\/main\/masks.zip<\/li>\n
- \u89e3\u538b\u5230 “try-off-anyone\/data\/zalando-hd-resized\/test\/” \u76ee\u5f55<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
\u6267\u884c\u6279\u91cf\u5904\u7406\uff1a<\/p>\n
python3 main.py --test\r\n<\/code><\/pre>\n3.2 \u8d28\u91cf\u8bc4\u4f30<\/h4>\n
\u7cfb\u7edf\u81ea\u52a8\u8ba1\u7b97\u4ee5\u4e0b\u6307\u6807\uff1a<\/p>\n
\n- SSIM\uff08\u7ed3\u6784\u76f8\u4f3c\u6027\uff09<\/li>\n
- LPIPS\uff08\u611f\u77e5\u76f8\u4f3c\u5ea6\uff09<\/li>\n
- FID\uff08Fr\u00e9chet Inception Distance\uff09<\/li>\n
- KID\uff08Kernel Inception Distance\uff09<\/li>\n<\/ul>\n
4. \u6ce8\u610f\u4e8b\u9879\u4e0e\u5efa\u8bae<\/h3>\n\n- \u8f93\u5165\u56fe\u7247\u5efa\u8bae\u4f7f\u7528\u6e05\u6670\u7684\u6b63\u9762\u670d\u88c5\u7167\u7247<\/li>\n
- \u5f53\u524d\u7248\u672c\u4e3b\u8981\u652f\u6301\u4e0a\u88c5\u5904\u7406<\/li>\n
- \u5efa\u8bae\u4f7f\u7528GPU\u8fdb\u884c\u5904\u7406\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd<\/li>\n
- \u5904\u7406\u5927\u91cf\u56fe\u7247\u65f6\u6ce8\u610f\u76d1\u63a7\u7cfb\u7edf\u8d44\u6e90\u4f7f\u7528\u60c5\u51b5<\/li>\n
- \u5b9a\u671f\u66f4\u65b0\u6a21\u578b\u548c\u4f9d\u8d56\u5305\u4ee5\u83b7\u5f97\u6700\u4f73\u6548\u679c<\/li>\n<\/ul>\n
5. \u5e38\u89c1\u95ee\u9898\u89e3\u51b3<\/h3>\n\n- \u5982\u679c\u9047\u5230CUDA\u76f8\u5173\u9519\u8bef\uff0c\u68c0\u67e5\uff1a\n
\n- GPU\u9a71\u52a8\u662f\u5426\u6b63\u786e\u5b89\u88c5<\/li>\n
- CUDA\u7248\u672c\u662f\u5426\u4e0ePyTorch\u7248\u672c\u5339\u914d<\/li>\n<\/ul>\n<\/li>\n
- \u56fe\u50cf\u5904\u7406\u8d28\u91cf\u95ee\u9898\uff1a\n
\n- \u8c03\u6574–steps\u53c2\u6570\u589e\u52a0\u5904\u7406\u6b65\u9aa4<\/li>\n
- \u9002\u5f53\u8c03\u6574–scale\u53c2\u6570\u6539\u5584\u6548\u679c<\/li>\n<\/ul>\n<\/li>\n
- \u5185\u5b58\u4e0d\u8db3\u95ee\u9898\uff1a\n
\n- \u964d\u4f4e\u6279\u5904\u7406\u6570\u91cf<\/li>\n
- \u51cf\u5c0f\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
TryOffAnyone\u662f\u4e00\u6b3e\u7a81\u7834\u6027\u7684AI\u56fe\u50cf\u5904\u7406\u5de5\u5177\uff0c\u4e13\u95e8\u7528\u4e8e\u89e3\u51b3\u7535\u5546\u9886\u57df\u4e2d\u670d\u88c5\u5c55\u793a\u7684\u96be\u9898\u3002\u5b83\u80fd\u591f\u5c06\u771f\u4eba\u7a7f\u7740\u72b6\u6001\u4e0b\u7684\u670d\u88c5\u7167\u7247\u667a\u80fd\u8f6c\u6362\u4e3a\u5e73\u94fa\u5c55\u793a\u6548\u679c\u56fe\uff0c\u8fd9\u9879\u6280\u672f\u57fa\u4e8e\u6700\u65b0\u7684Latent Diffusion Models(LDMs)\u3002\u8be5\u9879\u76ee\u7531\u7814…<\/p>\n","protected":false},"author":1,"featured_media":61544,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[410,412],"tags":[230,220],"class_list":["post-16968","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-art","category-image-editing","tag-aikaiyuanxiangmu","tag-aihuanlianyuhuanzhuang"],"_links":{"self":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts\/16968","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/comments?post=16968"}],"version-history":[{"count":0,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts\/16968\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/media\/61544"}],"wp:attachment":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/media?parent=16968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/categories?post=16968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/tags?post=16968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/p>\n
\u4f7f\u7528\u5e2e\u52a9<\/h2>\n1. \u73af\u5883\u914d\u7f6e\u4e0e\u5b89\u88c5<\/h3>\n
\u9996\u5148\u9700\u8981\u786e\u4fdd\u60a8\u7684\u7cfb\u7edf\u6ee1\u8db3\u4ee5\u4e0b\u8981\u6c42\uff1a<\/p>\n
- \n
- Python 3.x \u73af\u5883<\/li>\n
- CUDA\u652f\u6301\u7684GPU\uff08\u63a8\u8350\u7528\u4e8e\u52a0\u901f\u5904\u7406\uff09<\/li>\n
- Git\u7248\u672c\u63a7\u5236\u5de5\u5177<\/li>\n<\/ul>\n
\u5b89\u88c5\u6b65\u9aa4\uff1a<\/p>\n
# 1. \u514b\u9686\u9879\u76ee\u4ee3\u7801\r\ngit clone https:\/\/github.com\/ixarchakos\/try-off-anyone.git\r\ncd try-off-anyone\r\n# 2. \u5b89\u88c5\u4f9d\u8d56\u5305\r\npython3 -m pip install -r requirements.txt\r\n# 3. \u4e0b\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\r\n# \u8bbf\u95ee https:\/\/huggingface.co\/ixarchakos\/tryOffAnyone\r\n# \u5c06\u4e0b\u8f7d\u7684\u6a21\u578b\u6587\u4ef6\u653e\u7f6e\u5728 \"try-off-anyone\/ckpt\/\" \u76ee\u5f55\u4e0b\r\n<\/code><\/pre>\n2. \u57fa\u672c\u4f7f\u7528\u65b9\u6cd5<\/h3>\n
2.1 \u5904\u7406\u5355\u5f20\u5728\u7ebf\u56fe\u7247<\/h4>\n
python3 main.py --inference --url=\"\u60a8\u7684\u56fe\u7247URL\u5730\u5740\"\r\n<\/code><\/pre>\n\u5904\u7406\u540e\u7684\u56fe\u7247\u5c06\u4fdd\u5b58\u5728 “try-off-anyone\/data\/” \u76ee\u5f55\u4e0b\u3002<\/p>\n
2.2 \u53c2\u6570\u914d\u7f6e\u8bf4\u660e<\/h4>\n
- \n
- –seed\uff1a\u968f\u673a\u79cd\u5b50\u8bbe\u7f6e\uff08\u9ed8\u8ba4\uff1a36\uff09<\/li>\n
- –steps\uff1a\u5904\u7406\u6b65\u9aa4\u6570\uff08\u9ed8\u8ba4\uff1a50\uff09<\/li>\n
- –scale\uff1a\u7f29\u653e\u6bd4\u4f8b\uff08\u9ed8\u8ba4\uff1a2.5\uff09<\/li>\n
- –width\uff1a\u8f93\u51fa\u56fe\u50cf\u5bbd\u5ea6\uff08\u9ed8\u8ba4\uff1a384\uff09<\/li>\n
- –height\uff1a\u8f93\u51fa\u56fe\u50cf\u9ad8\u5ea6\uff08\u9ed8\u8ba4\uff1a512\uff09<\/li>\n
- –gpu_id\uff1a\u6307\u5b9aGPU\u8bbe\u5907ID\uff08\u9ed8\u8ba4\uff1a0\uff09<\/li>\n<\/ul>\n
3. \u9ad8\u7ea7\u529f\u80fd\u4f7f\u7528<\/h3>\n
3.1 \u6279\u91cf\u5904\u7406VITON-HD\u6570\u636e\u96c6<\/h4>\n
- \n
- \u4e0b\u8f7dVITON-HD\u539f\u59cb\u6570\u636e\u96c6<\/li>\n
- \u4e0b\u8f7d\u670d\u88c5\u56fe\u50cf\u8499\u7248\u6587\u4ef6\uff1a\n
- \n
- \u8bbf\u95ee\uff1ahttps:\/\/huggingface.co\/ixarchakos\/tryOffAnyone\/blob\/main\/masks.zip<\/li>\n
- \u89e3\u538b\u5230 “try-off-anyone\/data\/zalando-hd-resized\/test\/” \u76ee\u5f55<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
\u6267\u884c\u6279\u91cf\u5904\u7406\uff1a<\/p>\n
python3 main.py --test\r\n<\/code><\/pre>\n3.2 \u8d28\u91cf\u8bc4\u4f30<\/h4>\n
\u7cfb\u7edf\u81ea\u52a8\u8ba1\u7b97\u4ee5\u4e0b\u6307\u6807\uff1a<\/p>\n
- \n
- SSIM\uff08\u7ed3\u6784\u76f8\u4f3c\u6027\uff09<\/li>\n
- LPIPS\uff08\u611f\u77e5\u76f8\u4f3c\u5ea6\uff09<\/li>\n
- FID\uff08Fr\u00e9chet Inception Distance\uff09<\/li>\n
- KID\uff08Kernel Inception Distance\uff09<\/li>\n<\/ul>\n
4. \u6ce8\u610f\u4e8b\u9879\u4e0e\u5efa\u8bae<\/h3>\n
- \n
- \u8f93\u5165\u56fe\u7247\u5efa\u8bae\u4f7f\u7528\u6e05\u6670\u7684\u6b63\u9762\u670d\u88c5\u7167\u7247<\/li>\n
- \u5f53\u524d\u7248\u672c\u4e3b\u8981\u652f\u6301\u4e0a\u88c5\u5904\u7406<\/li>\n
- \u5efa\u8bae\u4f7f\u7528GPU\u8fdb\u884c\u5904\u7406\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd<\/li>\n
- \u5904\u7406\u5927\u91cf\u56fe\u7247\u65f6\u6ce8\u610f\u76d1\u63a7\u7cfb\u7edf\u8d44\u6e90\u4f7f\u7528\u60c5\u51b5<\/li>\n
- \u5b9a\u671f\u66f4\u65b0\u6a21\u578b\u548c\u4f9d\u8d56\u5305\u4ee5\u83b7\u5f97\u6700\u4f73\u6548\u679c<\/li>\n<\/ul>\n
5. \u5e38\u89c1\u95ee\u9898\u89e3\u51b3<\/h3>\n
- \n
- \u5982\u679c\u9047\u5230CUDA\u76f8\u5173\u9519\u8bef\uff0c\u68c0\u67e5\uff1a\n
- \n
- GPU\u9a71\u52a8\u662f\u5426\u6b63\u786e\u5b89\u88c5<\/li>\n
- CUDA\u7248\u672c\u662f\u5426\u4e0ePyTorch\u7248\u672c\u5339\u914d<\/li>\n<\/ul>\n<\/li>\n
- \u56fe\u50cf\u5904\u7406\u8d28\u91cf\u95ee\u9898\uff1a\n
- \n
- \u8c03\u6574–steps\u53c2\u6570\u589e\u52a0\u5904\u7406\u6b65\u9aa4<\/li>\n
- \u9002\u5f53\u8c03\u6574–scale\u53c2\u6570\u6539\u5584\u6548\u679c<\/li>\n<\/ul>\n<\/li>\n
- \u5185\u5b58\u4e0d\u8db3\u95ee\u9898\uff1a\n
- \n
- \u964d\u4f4e\u6279\u5904\u7406\u6570\u91cf<\/li>\n
- \u51cf\u5c0f\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
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