{"id":262,"date":"2022-09-04T13:58:37","date_gmt":"2022-09-04T04:58:37","guid":{"rendered":"https:\/\/sheltie-garage.xyz\/tech\/?p=262"},"modified":"2022-09-24T17:11:36","modified_gmt":"2022-09-24T08:11:36","slug":"stable-diffusion%e3%81%a7%e3%83%95%e3%82%a1%e3%82%a4%e3%83%b3%e3%83%81%e3%83%a5%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%e3%82%92%e8%a9%a6%e3%81%99%e3%81%8c%e3%80%81%e5%8b%95%e4%bd%9c%e3%81%97%e3%81%be","status":"publish","type":"post","link":"https:\/\/sheltie-garage.xyz\/tech\/2022\/09\/stable-diffusion%e3%81%a7%e3%83%95%e3%82%a1%e3%82%a4%e3%83%b3%e3%83%81%e3%83%a5%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%e3%82%92%e8%a9%a6%e3%81%99%e3%81%8c%e3%80%81%e5%8b%95%e4%bd%9c%e3%81%97%e3%81%be\/","title":{"rendered":"Stable Diffusion\u3067\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0(Finetune)\u3092\u8a66\u3059"},"content":{"rendered":"\n

\u307e\u305f\u307e\u305fStable Diffusion\u30cd\u30bf\u3067\u3059\u3002\u30c9\u306f\u307e\u308a\u3057\u305f\u306e\u3067\u3001\u3057\u3070\u3089\u304f\u306f\u3053\u306e\u8a71\u984c\u306b\u306a\u308a\u305d\u3046\u3067\u3059<\/p>\n\n\n\n

\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3068\u306f\uff1f<\/h2>\n\n\n\n

\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0(\u8ee2\u79fb\u5b66\u7fd2)\u3068\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u3082\u306e\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

\u4e00\u822c\u7684\u306a\u624b\u6cd5\u3067\u3042\u308b\u300e\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u300f\u306f\u3001\u3042\u3089\u304b\u3058\u3081\u6b63\u89e3\u3092\u4e0e\u3048\u3066\u5b66\u7fd2\u3092\u884c\u3046\u30d5\u30ed\u30fc\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u6b63\u89e3\u3092\u5c0e\u304f\u305f\u3081\u306b\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u304c\u5fc5\u8981\u3068\u306a\u308a\u3001\u304b\u3064\u5b66\u7fd2\u6642\u9593\u3082\u81a8\u5927\u306b\u304b\u304b\u308b\u306e\u304c\u96e3\u70b9\u3067\u3059\u3002
<\/p>

\u4e00\u65b9\u3067\u8ee2\u79fb\u5b66\u7fd2\u306f\u3001\u30bf\u30b9\u30af\u306e\u4eee\u8aac\u7acb\u3066\u3092\u884c\u3046\u3046\u3048\u3067\u3001\u5225\u306e\u30bf\u30b9\u30af\u3067\u3059\u3067\u306b\u5b66\u7fd2\u3055\u308c\u305f\u77e5\u8b58\u3092\u8ee2\u7528\u3057\u3001\u4eee\u8aac\u7acb\u3066\u3092\u884c\u308f\u305b\u308b\u624b\u6cd5\u3068\u306a\u308a\u307e\u3059\u3002\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3068\u5b66\u7fd2\u306b\u304b\u304b\u308b\u6642\u9593\u3092\u5927\u5e45\u306b\u524a\u6e1b\u3067\u304d\u308b\u305f\u3081\u3001\u975e\u5e38\u306b\u52b9\u7387\u7684\u306a\u624b\u6cd5\u3068\u3057\u3066\u8a55\u4fa1\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\u8ee2\u79fb\u5b66\u7fd2\u3068\u306f\uff1f\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3068\u306e\u9055\u3044\u3084\u6d3b\u7528\u4f8b\u3092\u3054\u7d39\u4ecb – \u9867\u554f\u3001\u5c02\u9580\u5bb6\u306a\u3069\u306e\u30d7\u30ed\u4eba\u6750\u7d39\u4ecb\u30b5\u30fc\u30d3\u30b9 (hipro-job.jp)<\/a><\/cite><\/blockquote>\n\n\n\n

Stable Diffusion\u306e\u5834\u5408\u3001\u6c4e\u7528\u7684\u306a\u30a4\u30e9\u30b9\u30c8\u751f\u6210\u30e2\u30c7\u30eb\u6545\u3001\u7279\u5b9a\u306e\u5206\u91ce\u306e\u30a4\u30e9\u30b9\u30c8\u3092\u751f\u6210\u3057\u3088\u3046\u3068\u3057\u305f\u5834\u5408\u3001\u306a\u304b\u306a\u304b\u3046\u307e\u304f\u3044\u304b\u306a\u3044\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u5177\u4f53\u7684\u306b\u306f\u30a2\u30cb\u30e1\u7cfb\u306e\u30a4\u30e9\u30b9\u30c8\u751f\u6210\u3067\u3059\u306d\u3002<\/p>\n\n\n\n

\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3046\u3053\u3068\u3067\u3001\u9ad8\u5ea6\u306a\u30a4\u30e9\u30b9\u30c8\u3092\u751f\u6210\u3067\u304d\u308b\u7279\u5fb4\u3092\u751f\u304b\u3057\u3064\u3064\u3001\u30a2\u30cb\u30e1\u7cfb\u306b\u7279\u5316\u3057\u305f\u30e2\u30c7\u30eb\u3082\u4f5c\u6210\u3067\u304d\u308b\u306e\u3067\u306f\u3068\u601d\u3044\u3001\u5b9f\u9a13\u3057\u3066\u307f\u308b\u3053\u3068\u306b\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

\u3059\u3067\u306b\u5b9f\u65bd\u3055\u308c\u3066\u3044\u308b\u65b9\u306e\u30d6\u30ed\u30b0<\/a>\u3067\u306f\u3001\u300c\u3044\u3089\u3059\u3068\u3084\u300d\u3067\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u3001\u7d50\u679c\u3092\u8868\u793a\u3057\u3066\u3044\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

\u30b5\u30fc\u30d3\u30b9\u9589\u9396\u3057\u3066\u3057\u307e\u3063\u305f\u300cmimic<\/a>\u300d\u3082\u3001\u304a\u305d\u3089\u304f\u540c\u3058\u6280\u8853\u3092\u5229\u7528\u3057\u3066\u30a4\u30e9\u30b9\u30c8\u3092\u751f\u6210\u3057\u3066\u3044\u308b\u3068\u601d\u308f\u308c\u307e\u3059\u3002<\/p>\n\n\n\n

\u65e9\u901f\u3084\u3063\u3066\u307f\u308b<\/h2>\n\n\n\n

\u74b0\u5883\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059
\u30fbOS:Windows11
\u30fb CPU:Corei7 9700K
\u30fbGPU:RTX2080 VRAM 8GB<\/p>\n\n\n\n

\u53ef\u80fd\u3067\u3042\u308c\u3070Linux\u74b0\u5883\u3067\u52d5\u304b\u3059\u3053\u3068\u3092\u63a8\u5968\u3057\u307e\u3059<\/strong><\/p>\n\n\n\n

\u3053\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306b\u306fUnix\u74b0\u5883\u3067\u3057\u304b\u5229\u7528\u3067\u304d\u306a\u3044\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u5229\u7528\u3055\u308c\u3066\u304a\u308a\u3001\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u3084\u30a8\u30e9\u30fc\u4fee\u6b63\u3067\u7cbe\u795e\u3092\u524a\u304c\u308c\u307e\u3059<\/p>\n\n\n\n

\u307e\u305f\u3001\u3044\u308d\u3044\u308d\u3068\u30d7\u30ed\u30b0\u30e9\u30e0\u4fee\u6b63\u3092\u3057\u306a\u3044\u3068\u52d5\u304b\u306a\u304b\u3063\u305f\u306e\u3067\u3001\u5148\u306b\u30da\u30fc\u30b8\u4e0b\u90e8\u306e\u30a8\u30e9\u30fc\u96c6\u3092\u898b\u3066\u304a\u304f\u3068\u3088\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093<\/p>\n\n\n\n

\u305d\u308c\u3067\u3082Windows\u3067\u52d5\u304b\u3057\u305f\u3044\u3068\u3044\u3046\u65b9\u306f\u304a\u9032\u307f\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n

\u30fb\u30fb\u30fb<\/p>\n\n\n\n

reddit\u306e\u6295\u7a3f\u306b\u3084\u308a\u65b9\u304c\u66f8\u3044\u3066\u3042\u308b\u305f\u3081\u3001\u305d\u308c\u306b\u5f93\u3063\u3066\u9032\u3081\u3066\u3044\u304d\u307e\u3059
https:\/\/www.reddit.com\/r\/StableDiffusion\/comments\/wvzr7s\/tutorial_fine_tuning_stable_diffusion_using_only\/<\/a><\/p>\n\n\n\n

\u74b0\u5883\u69cb\u7bc9<\/h3>\n\n\n\n

\u6307\u5b9a\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u30ed\u30fc\u30ab\u30eb\u306b\u30af\u30ed\u30fc\u30f3\u3057\u307e\u3059\u3002\u30af\u30ed\u30fc\u30f3\u5f8c\u3001\u30d5\u30a9\u30eb\u30c0\u79fb\u52d5\u3057\u3001conda\u30b3\u30de\u30f3\u30c9\u3067\u74b0\u5883\u3092\u69cb\u7bc9\u3057\u307e\u3059<\/p>\n\n\n\n

Windows\u74b0\u5883\u306bAnaconda\u3001Git \u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u524d\u63d0\u306a\u306e\u3067\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u904e\u53bb\u8a18\u4e8b<\/a>\u3082\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044<\/p>\n\n\n\n

git clone https:\/\/github.com\/rinongal\/textual_inversion.git\ncd textual_inversion\n# conda remove -n ldm --all  # \u3059\u3067\u306bldm\u306e\u4eee\u60f3\u74b0\u5883\u304c\u5b58\u5728\u3059\u308b\u5834\u5408\u3001\u3044\u3063\u305f\u3093\u524a\u9664\u3059\u308b\nconda env create -f environment.yaml\nconda activate ldm\npip install -e .<\/code><\/pre><\/div>\n\n\n\n
\u5b66\u7fd2\u7528\u753b\u50cf\u306e\u6e96\u5099<\/h2>\n\n\n\n
\u624b\u9806\u306b\u3088\u308c\u3070\u300c512px \u00d7 512px\u306e\u753b\u50cf5\u679a\u304c\u5fc5\u8981\u300d\u3068\u306e\u3053\u3068\u3067\u3057\u305f\u3002
\u4eca\u56de\u306fmimic\u30af\u30ed\u30fc\u30ba\u30d9\u30fc\u30bf\u306b\u53c2\u52a0\u3055\u308c\u3066\u3044\u305f\u300c\u305f\u3093\u305f\u3093\u3081\u3093<\/a>\u300d\u3055\u3093\u306e\u753b\u50cf\u3092\u5229\u7528\u3055\u305b\u3066\u3044\u305f\u3060\u304d\u3001\u5b66\u7fd2\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3057\u305f\u3002
\u304b\u308f\u3044\u3044\u30a4\u30e9\u30b9\u30c8\u304c\u591a\u304f\u3066\u3001\u5b66\u7fd2\u7528\u753b\u50cf\u3092\u9078\u5b9a\u3059\u308b\u306e\u306b\u6642\u9593\u3092\u53d6\u3089\u308c\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u30fb\u30fb\u30fb<\/p>\n\n\n\n
\u5b66\u7fd2\u7528\u753b\u50cf\u306f\u3001\u3072\u3068\u307e\u305a\u9854\u306e\u90e8\u5206\u306e\u307f512 \u00d7 512\u306e\u5927\u304d\u3055\u306b\u5207\u308a\u3060\u3057\u3066\u6e96\u5099\u3057\u307e\u3057\u305f<\/p>\n\n\n\n
\u753b\u50cf\u306fGimp\u3092\u4f7f\u3063\u3066\u30c8\u30ea\u30df\u30f3\u30b0\u3092\u884c\u3044\u30015\u679a\u306e\u5b66\u7fd2\u7528\u753b\u50cf\u3092\u4f5c\u6210\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u304a\u5bdf\u3057\u306e\u901a\u308a\u3001\u30b1\u30e2\u30df\u30df\u597d\u304d\u3067\u3059\u30fb\u30fb\u30fb<\/p>\n\n\n\n
\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306e\u5b9f\u65bd<\/h3>\n\n\n\n
\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u65bd\u3059\u308b\u524d\u306b\u3001\u6e96\u5099\u3092\u3057\u307e\u3059<\/p>\n\n\n\n
\u30fb\u6e96\u50991:Stable Diffusion\u30e2\u30c7\u30eb\u306e\u6e96\u5099
\u3000StableDiffusion V1.4\u306e\u30e2\u30c7\u30eb\u304c\u5fc5\u8981\u3067\u3059\u3002HuggingFace\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u3082\u3088\u3044\u3067\u3059\u3057\u3001Stable Diffusion\u306e\u74b0\u5883\u304c\u69cb\u7bc9\u6e08\u307f\u3067\u3042\u308c\u3070\u3001\u30b3\u30d4\u30fc\u3057\u3066\u9069\u5f53\u306a\u30d5\u30a9\u30eb\u30c0\u306b\u4fdd\u5b58\u3057\u3066\u304a\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u30fb\u6e96\u50992:initializer_words \u306e\u6e96\u5099
\u3000configs\/stable-diffusion\/v1-finetune.yaml \u3092\u30c6\u30ad\u30b9\u30c8\u30a8\u30c7\u30a3\u30bf\u306a\u3069\u3067\u958b\u304d\u3001initializer_words\u306e\u8a2d\u5b9a\u90e8\u5206\u3092\u63a2\u3057\u307e\u3059\u3002
\u3000\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u91cd\u8981\u3067\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u5185\u5bb9\u3092\u8aac\u660e\u3059\u308b\u7c21\u5358\u306a\u8a9e\u53e5\u306e\u7f85\u5217\u3092\u6307\u5b9a\u3059\u308b\u3088\u3046\u3067\u3001\u8907\u6570\u6307\u5b9a\u3059\u308b\u5834\u5408\u306f2\u91cd\u5f15\u7528\u7b26\u3067\u56f2\u307f\u3001\u30ab\u30f3\u30de\u3067\u533a\u5207\u308b\u3088\u3046\u3067\u3059\u3002\u4eca\u56de\u306f\u4ee5\u4e0b\u306e\u753b\u50cf\u306e\u3088\u3046\u306b\u6307\u5b9a\u3057\u3066\u307f\u307e\u3057\u305f<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u6e96\u5099\u304c\u3067\u304d\u305f\u306e\u3067\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059
\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067\u5b9f\u65bd\u3057\u3066\u307f\u307e\u3057\u305f<\/p>\n\n\n\n
python main.py --base configs\/stable-diffusion\/v1-finetune.yaml -t --actual_resume .\/models\/ldm\/stable-diffusion-v1\/sd-v1-4.ckpt -n tantanmen_train --gpus 1, --data_root .\/LerningImages\/512<\/code><\/pre><\/div>\n\n\n\n
\u4f7f\u3063\u3066\u307f\u308b<\/h2>\n\n\n\n
\u4ee5\u4e0a\u3067\u65b0\u3057\u3044\u7279\u5fb4\u3092\u899a\u3048\u305f\u30e2\u30c7\u30eb\u304c\u751f\u6210\u3055\u308c\u305f\u306f\u305a\u306a\u306e\u3067\u3001\u5b9f\u969b\u306b\u5b9f\u884c\u3057\u3066\u307f\u307e\u3059<\/p>\n\n\n\n
python scripts\/stable_txt2img.py --ddim_eta 0.0 --n_samples 1 --n_iter 2 --scale 10.0 --ddim_steps 50 --embedding_path .\/logs\/5122022-09-05T08-03-26_tantanmen_train\/checkpoints\/embeddings.pt --ckpt .\/models\/ldm\/stable-diffusion-v1\/sd-v1-4.ckpt --config .\/logs\/5122022-09-05T08-03-26_tantanmen_train\/configs\/5122022-09-05T08-03-26-project.yaml --prompt "anime, girl, japanese, *"<\/code><\/pre><\/div>\n\n\n\n
\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u5404\u500b\u4eba\u306e\u74b0\u5883\u3067\u30d5\u30a1\u30a4\u30eb\u540d\u304c\u7570\u306a\u3063\u3066\u304f\u308b\u306e\u3067\u3001\u305d\u306e\u3042\u305f\u308a\u306f\u3054\u6ce8\u610f\u304f\u3060\u3055\u3044\u3002
GPU\u30e1\u30e2\u30ea\u4e0d\u8db3\u3067\u3001n_samples\u306b2\u4ee5\u4e0a\u306e\u5024\u3092\u5165\u529b\u3059\u308b\u3068\u30e1\u30e2\u30ea\u4e0d\u8db3\u3067\u843d\u3061\u307e\u3057\u305f\u30fb\u30fb\u30fb
n_samples=1\u3067\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3001\u3072\u3068\u307e\u305a\u30a4\u30e9\u30b9\u30c8\u306f\u751f\u6210\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u751f\u6210\u3055\u308c\u305f\u30a4\u30e9\u30b9\u30c8<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u7d20\u306eStable Diffusion\u3067\u751f\u6210\u3059\u308b\u3088\u308a\u3082\u307e\u3068\u3082\u306a(\u6bd4\u8f03\u7684\u304b\u308f\u3044\u3044)\u7d50\u679c\u304c\u51fa\u529b\u3055\u308c\u308b\u3051\u3069\u3001\u5165\u529b\u753b\u50cf\u306b\u306f\u7a0b\u9060\u3044\u753b\u50cf\u304c\u51fa\u529b\u3055\u308c\u305f<\/p>\n\n\n\n
\u304c\u3001\u3072\u3068\u307e\u305a\u4eca\u56de\u306e\u7d50\u8ad6\u3002
\u30d1\u30e9\u30e1\u30fc\u30bf\u306a\u3069\u3044\u3058\u308c\u308c\u3070\u3001\u307e\u305f\u7d50\u679c\u306f\u5909\u308f\u308b\u304b\u3082\u3057\u308c\u306a\u3044\u3051\u3069\u3001VRAM8GB\u3067\u306f\u3044\u308d\u3044\u308d\u9650\u754c\u306a\u3093\u3067\u3059\u30fb\u30fb\u30fb
\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30ba\u7248\u304c\u51fa\u308b\u304b\u3001\u3064\u3088\u3064\u3088GPU\u3092\u6301\u3063\u3066\u3044\u308b\u65b9\u304c\u3044\u308d\u3044\u308d\u5b9f\u9a13\u3055\u308c\u308b\u306e\u3092\u5f85\u3064\u3053\u3068\u306b\u3057\u307e\u3059\u30fb\u30fb\u30fb<\/p>\n\n\n\n
\u8ffd\u4f38<\/h2>\n\n\n\n
mimic\u3063\u3066\u3001\u3061\u3083\u3093\u3068\u5b66\u7fd2\u753b\u50cf\u306b\u8fd1\u3044\u753b\u50cf\u3092\u751f\u6210\u3067\u304d\u3066\u3044\u305f\u306e\u3067\u3001\u5272\u3068\u9ad8\u5ea6\u306a\u3053\u3068\u3092\u3084\u3063\u3066\u308b\u3093\u3060\u306a\u3041\u3068\u601d\u3063\u305f<\/p>\n\n\n\n
\u30a8\u30e9\u30fc\u96c6<\/h2>\n\n\n\n
Traceback (most recent call last):\n  File \"main.py\", line 762, in <module>\n    ngpu = len(lightning_config.trainer.gpus.strip(\",\").split(','))\nAttributeError: 'int' object has no attribute 'strip'<\/pre>\n\n\n\n
main.py 762\u884c\u76ee\u3042\u305f\u308a\u3067ngpu\u306b\u5fc5\u305a1\u3092\u4ee3\u5165\u3059\u308b\u3088\u3046\u306b\u4fee\u6b63\u3057\u3066\u56de\u907f<\/p>\n\n\n\n
        if not cpu:\n            # ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))\n            ngpu = 1\n        else:\n            ngpu = 1<\/code><\/pre><\/div>\n\n\n\n
Traceback (most recent call last):\n  File \"main.py\", line 800, in <module>\n    signal.signal(signal.SIGUSR1, melk)\nAttributeError: module 'signal' has no attribute 'SIGUSR1'<\/pre>\n\n\n\n
main.py 800\u884c\u76ee\u3042\u305f\u308a\u306esignal\u51e6\u7406\u4e00\u5f0f\u3092\u5909\u66f4<\/p>\n\n\n\n
        import signal\n\n        signal.signal(signal.SIGTERM, melk)\n        signal.signal(signal.SIGTERM, divein)<\/code><\/pre><\/div>\n\n\n\n
Traceback (most recent call last):\n  File \"main.py\", line 806, in <module>\n    trainer.fit(model, data)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 740, in fit\n    self._call_and_handle_interrupt(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 685, in _call_and_handle_interrupt\n    return trainer_fn(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 777, in _fit_impl\n    self._run(model, ckpt_path=ckpt_path)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1199, in _run\n    self._dispatch()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1279, in _dispatch\n    self.training_type_plugin.start_training(self)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\training_type_plugin.py\", line 202, in start_training\n    self._results = trainer.run_stage()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1289, in run_stage\n    return self._run_train()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1311, in _run_train\n    self._run_sanity_check(self.lightning_module)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1375, in _run_sanity_check\n    self._evaluation_loop.run()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\", line 145, in run    self.advance(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\dataloader\\evaluation_loop.py\", line 110, in advance\n    dl_outputs = self.epoch_loop.run(dataloader, dataloader_idx, dl_max_batches, self.num_dataloaders)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\", line 145, in run    self.advance(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\evaluation_epoch_loop.py\", line 122, in advance\n    output = self._evaluation_step(batch, batch_idx, dataloader_idx)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\evaluation_epoch_loop.py\", line 217, in _evaluation_step\n    output = self.trainer.accelerator.validation_step(step_kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\accelerators\\accelerator.py\", line 236, in validation_step\n    return self.training_type_plugin.validation_step(*step_kwargs.values())\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\training_type_plugin.py\", line 219, in validation_step\n    return self.model.validation_step(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\autograd\\grad_mode.py\", line 28, in decorate_context\n    return func(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 368, in validation_step\n    _, loss_dict_no_ema = self.shared_step(batch)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 907, in shared_step\n    loss = self(x, c)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 915, in forward\n    c = self.get_learned_conditioning(c)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 594, in get_learned_conditioning    c = self.cond_stage_model.encode(c, embedding_manager=self.embedding_manager)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\encoders\\modules.py\", line 324, in encode\n    return self(text, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\encoders\\modules.py\", line 319, in forward\n    z = self.transformer(input_ids=tokens, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\encoders\\modules.py\", line 297, in transformer_forward\n    return self.text_model(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\encoders\\modules.py\", line 258, in text_encoder_forward\n    hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager)  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\encoders\\modules.py\", line 180, in embedding_forward\n    inputs_embeds = self.token_embedding(input_ids)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\sparse.py\", line 158, in forward\n    return F.embedding(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\functional.py\", line 2044, in embedding\n    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)\nRuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper__index_select)<\/pre>\n\n\n\n
\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u300c–gpus 0,\u3000\u2192\u3000–gpus 1,\u300d\u306b\u5909\u66f4\u3057\u305f\u3089\u3072\u3068\u307e\u305a\u30a8\u30e9\u30fc\u306f\u6d88\u3048\u307e\u3057\u305f<\/p>\n\n\n\n
\u3053\u3061\u3089\u306f\u5404\u3005\u306e\u74b0\u5883\u3067\u6307\u5b9a\u3059\u308b\u756a\u53f7\u304c\u7570\u306a\u308b\u3088\u3046\u3067\u3059\u306e\u3067\u3001gpu 0,\u3000\u2192\u3000gpu 1,\u3000\u3068\u3044\u3046\u3088\u3046\u306b0\u756a\u304b\u3089\u3057\u3066\u3044\u3066\u3046\u307e\u304f\u52d5\u304f\u756a\u53f7\u3092\u63a2\u308b\u306e\u304c\u3088\u3055\u305d\u3046\u3067\u3059<\/strong><\/p>\n\n\n\n
Traceback (most recent call last):\n  File \"main.py\", line 806, in <module>\n    trainer.fit(model, data)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 740, in fit\n    self._call_and_handle_interrupt(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 685, in _call_and_handle_interrupt\n    return trainer_fn(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 777, in _fit_impl\n    self._run(model, ckpt_path=ckpt_path)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1137, in _run\n    self.accelerator.setup_environment()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\accelerators\\gpu.py\", line 39, in setup_environment\n    super().setup_environment()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\accelerators\\accelerator.py\", line 83, in setup_environment\n    self.training_type_plugin.setup_environment()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\ddp.py\", line 185, in setup_environment\n    self.setup_distributed()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\ddp.py\", line 272, in setup_distributed\n    init_dist_connection(self.cluster_environment, self.torch_distributed_backend)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\utilities\\distributed.py\", line 387, in init_dist_connection\n    torch.distributed.init_process_group(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\distributed\\distributed_c10d.py\", line 583, in init_process_group\n    default_pg = _new_process_group_helper(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\distributed\\distributed_c10d.py\", line 708, in _new_process_group_helper\n    raise RuntimeError(\"Distributed package doesn't have NCCL \" \"built in\")\nRuntimeError: Distributed package doesn't have NCCL built in<\/pre>\n\n\n\n
\u3053\u3061\u3089\u306e\u8a18\u4e8b<\/a>\u3092\u53c2\u8003\u306b\u3001main.py\u3092\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u4fee\u6b63<\/p>\n\n\n\n
if __name__ == "__main__":\n    # custom parser to specify config files, train, test and debug mode,\n    # postfix, resume.\n    # `--key value` arguments are interpreted as arguments to the trainer.\n    # `nested.key=value` arguments are interpreted as config parameters.\n    # configs are merged from left-to-right followed by command line parameters.\n\n\u3000\u3000\u30fb\u30fb\u30fb\u7701\u7565\u30fb\u30fb\u30fb\n\n    os.environ["PL_TORCH_DISTRIBUTED_BACKEND"] = "gloo"  \u2190\u3053\u308c\u3092\u8ffd\u52a0\n\n    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")\n\n    # add cwd for convenience and to make classes in this file available when\n    # running as `python main.py`\n    # (in particular `main.DataModuleFromConfig`)<\/code><\/pre><\/div>\n\n\n\n
Traceback (most recent call last):\n  File \"main.py\", line 808, in <module>\n    trainer.fit(model, data)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 740, in fit\n    self._call_and_handle_interrupt(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 685, in _call_and_handle_interrupt\n    return trainer_fn(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 777, in _fit_impl\n    self._run(model, ckpt_path=ckpt_path)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1199, in _run\n    self._dispatch()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1279, in _dispatch\n    self.training_type_plugin.start_training(self)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\training_type_plugin.py\", line 202, in start_training\n    self._results = trainer.run_stage()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1289, in run_stage\n    return self._run_train()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\", line 1319, in _run_train\n    self.fit_loop.run()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\", line 145, in run\n    self.advance(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\fit_loop.py\", line 234, in advance\n    self.epoch_loop.run(data_fetcher)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\", line 145, in run\n    self.advance(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\training_epoch_loop.py\", line 193, in advance\n    batch_output = self.batch_loop.run(batch, batch_idx)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\", line 145, in run\n    self.advance(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\batch\\training_batch_loop.py\", line 88, in advance\n    outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\", line 145, in run\n    self.advance(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\optimization\\optimizer_loop.py\", line 215, in advance\n    result = self._run_optimization(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\optimization\\optimizer_loop.py\", line 266, in _run_optimization\n    self._optimizer_step(optimizer, opt_idx, batch_idx, closure)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\optimization\\optimizer_loop.py\", line 378, in _optimizer_step\n    lightning_module.optimizer_step(\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\core\\lightning.py\", line 1652, in optimizer_step\n    optimizer.step(closure=optimizer_closure)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\core\\optimizer.py\", line 164, in step\n    trainer.accelerator.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\accelerators\\accelerator.py\", line 336, in optimizer_step\n    self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\precision\\precision_plugin.py\", line 163, in optimizer_step\n    optimizer.step(closure=closure, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\optim\\optimizer.py\", line 88, in wrapper\n    return func(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\autograd\\grad_mode.py\", line 28, in decorate_context\n    return func(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\optim\\adamw.py\", line 92, in step\n    loss = closure()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\precision\\precision_plugin.py\", line 148, in _wrap_closure\n    closure_result = closure()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\optimization\\optimizer_loop.py\", line 160, in __call__\n    self._result = self.closure(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\optimization\\optimizer_loop.py\", line 142, in closure\n    step_output = self._step_fn()\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\loops\\optimization\\optimizer_loop.py\", line 435, in _training_step\n    training_step_output = self.trainer.accelerator.training_step(step_kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\accelerators\\accelerator.py\", line 216, in training_step\n    return self.training_type_plugin.training_step(*step_kwargs.values())\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\ddp.py\", line 439, in training_step\n    return self.model(*args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\parallel\\distributed.py\", line 886, in forward\n    output = self.module(*inputs[0], **kwargs[0])\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\pytorch_lightning\\overrides\\base.py\", line 81, in forward\n    output = self.module.training_step(*inputs, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 352, in training_step\n    loss, loss_dict = self.shared_step(batch)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 907, in shared_step\n    loss = self(x, c)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 920, in forward\n    return self.p_losses(x, c, t, *args, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 1056, in p_losses\n    model_output = self.apply_model(x_noisy, t, cond)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 1028, in apply_model\n    x_recon = self.model(x_noisy, t, **cond)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\models\\diffusion\\ddpm.py\", line 1513, in forward\n    out = self.diffusion_model(x, t, context=cc)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\diffusionmodules\\openaimodel.py\", line 732, in forward\n    h = module(h, emb, context)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\diffusionmodules\\openaimodel.py\", line 85, in forward\n    x = layer(x, context)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\attention.py\", line 258, in forward\n    x = block(x, context=context)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\attention.py\", line 209, in forward\n    return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\diffusionmodules\\util.py\", line 116, in checkpoint\n    return func(*inputs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\attention.py\", line 213, in _forward\n    x = self.attn2(self.norm2(x), context=context) + x\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\textual_inversion\\ldm\\modules\\attention.py\", line 175, in forward\n    k = self.to_k(context)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1102, in _call_impl\n    return forward_call(*input, **kwargs)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\modules\\linear.py\", line 103, in forward\n    return F.linear(input, self.weight, self.bias)\n  File \"F:\\Project\\MachineLerning\\Anaconda\\envs\\ldm\\lib\\site-packages\\torch\\nn\\functional.py\", line 1848, in linear\n    return torch._C._nn.linear(input, weight, bias)\nRuntimeError: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 8.00 GiB total capacity; 7.20 GiB already allocated; 0 bytes free; 7.29 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF<\/pre>\n\n\n\n
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Comment<\/a> from discussion jasoa’s comment from discussion "[Tutorial] "Fine Tuning" Stable Diffusion using only 5 Images Using Textual Inversion."<\/a>.<\/div>