{"id":625,"date":"2024-01-16T11:24:36","date_gmt":"2024-01-16T02:24:36","guid":{"rendered":"https:\/\/rebron.net\/blog\/?p=625"},"modified":"2024-01-16T11:24:37","modified_gmt":"2024-01-16T02:24:37","slug":"%e3%80%90%e6%9c%80%e6%96%b0%e7%89%88%e3%80%91stablediffusion%e3%82%92%e7%84%a1%e6%96%99%e3%81%aegoogle-colab%e3%81%a7%e5%8b%95%e3%81%8b%e3%81%99%e6%96%b9%e6%b3%95%e3%80%90%e7%94%9f%e6%88%90ai%e3%80%91","status":"publish","type":"post","link":"https:\/\/rebron.net\/blog\/%e3%80%90%e6%9c%80%e6%96%b0%e7%89%88%e3%80%91stablediffusion%e3%82%92%e7%84%a1%e6%96%99%e3%81%aegoogle-colab%e3%81%a7%e5%8b%95%e3%81%8b%e3%81%99%e6%96%b9%e6%b3%95%e3%80%90%e7%94%9f%e6%88%90ai%e3%80%91\/","title":{"rendered":"\u3010\u6700\u65b0\u7248\u3011StableDiffusion\u3092\u7121\u6599\u306eGoogle Colab\u3067\u52d5\u304b\u3059\u65b9\u6cd5\u3010\u751f\u6210AI\u3011"},"content":{"rendered":"\n

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

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

\u65b0\u898f\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306e\u4f5c\u6210<\/h2>\n\n\n\n

Googlecolab<\/a>\u306b\u30a2\u30af\u30bb\u30b9\u3057\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u300c\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u3092\u65b0\u898f\u4f5c\u6210\u300d\u30dc\u30bf\u30f3\u3092\u30af\u30ea\u30c3\u30af\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

GPU\u306b\u5909\u66f4<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4ee5\u4e0b\u306e\u753b\u50cf\u306e\u3088\u3046\u306b\u3001\u300cGPU\u300d\u3068\u66f8\u3044\u3066\u3042\u308b\u9805\u76ee\u3092\u9078\u629e\u3057\u307e\u3059\u3002
\u9078\u629e\u3067\u304d\u308b\u9805\u76ee\u306f\u6642\u9593\u306b\u3088\u3063\u3066\u7570\u306a\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002
\u4eca\u56de\u306f\u300cT4 GPU\u300d\u3092\u9078\u3073\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

GPU\u306b\u5909\u66f4\u3055\u308c\u305f\u3053\u3068\u306e\u78ba\u8a8d\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u6253\u3061\u3001\u300c\u518d\u751f\u30dc\u30bf\u30f3\u300d\u304b\u300cShift+Enter\u300d\u3067\u5b9f\u884c\u3057\u307e\u3059\u3002
\u300c!nvidia-smi\u300d\u306fGPU\u306e\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u3092\u884c\u3046\u30b3\u30de\u30f3\u30c9\u3067\u3059\u3002<\/p>\n\n\n\n

!nvidia-smi<\/code><\/pre>\n\n\n\n
\u4ee5\u4e0b\u306e\u753b\u50cf\u306e\u3088\u3046\u306b\u300cNVIDIA-SMI \u6570\u5b57\u300d\u3068\u8868\u793a\u3055\u308c\u3066\u3044\u308c\u3070\u5927\u4e08\u592b\u3067\u3059\u3002
\u300cNVIDIA-SMI has failed\u300d\u3068\u8868\u793a\u3055\u308c\u308b\u969b\u306f\u3001\u518d\u5ea6GPU\u306b\u306a\u3063\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n\n\n\n
\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067Python\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u7ba1\u7406\u30c4\u30fc\u30eb\u3067\u3042\u308bpip\u3092\u4f7f\u3063\u3066\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002\u30b3\u30de\u30f3\u30c9\u306f\u300c!nvidia-smi\u300d\u306e\u6b21\u306e\u5165\u529b\u67a0\u306b\u5165\u529b\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
    \n
  1. diffusers<\/strong>: Diffusers\u306f\u3001\u4e3b\u306b\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306b\u95a2\u9023\u3059\u308b\u69d8\u3005\u306a\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u30e2\u30c7\u30eb\u306e\u62e1\u6563\u30d7\u30ed\u30bb\u30b9\u3092\u6271\u3046\u305f\u3081\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u62e1\u6563\u30e2\u30c7\u30eb\u306f\u3001\u751f\u6210\u30e2\u30c7\u30eb\u306e\u4e00\u7a2e\u3067\u3042\u308a\u3001\u7279\u306b\u30c6\u30ad\u30b9\u30c8\u3001\u753b\u50cf\u3001\u97f3\u58f0\u306a\u3069\u306e\u751f\u6210\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/li>\n\n\n\n
  2. transformers<\/strong>: Transformers\u306f\u3001\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306b\u304a\u3051\u308b\u6700\u5148\u7aef\u306e\u30e2\u30c7\u30eb\u3092\u7c21\u5358\u306b\u5229\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u3059\u308b\u305f\u3081\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u306f\u30c6\u30ad\u30b9\u30c8\u306e\u5206\u985e\u3001\u751f\u6210\u3001\u7ffb\u8a33\u3001\u8981\u7d04\u306a\u3069\u69d8\u3005\u306a\u30bf\u30b9\u30af\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/li>\n\n\n\n
  3. ftfy<\/strong>: FTFY\uff08Fix Text For You\uff09\u306f\u3001\u58ca\u308c\u305f\u30c6\u30ad\u30b9\u30c8\u3092\u4fee\u6b63\u3059\u308b\u305f\u3081\u306e\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u6587\u5b57\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u306e\u554f\u984c\u3092\u81ea\u52d5\u7684\u306b\u4fee\u6b63\u3057\u3001\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u3088\u308a\u30af\u30ea\u30fc\u30f3\u3067\u8aad\u307f\u3084\u3059\u3044\u5f62\u306b\u5909\u63db\u3057\u307e\u3059\u3002<\/li>\n\n\n\n
  4. accelerate<\/strong>: Accelerate\u306f\u3001\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3084\u63a8\u8ad6\u3092\u7c21\u5358\u306b\u304b\u3064\u52b9\u7387\u7684\u306b\u3059\u308b\u305f\u3081\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u7279\u306b\u3001GPU\u3084TPU\u306a\u3069\u306e\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u30a2\u30af\u30bb\u30e9\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u6d3b\u7528\u3059\u308b\u969b\u306b\u6709\u7528\u3067\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
    !pip install diffusers transformers ftfy accelerate<\/code><\/pre>\n\n\n\n
    \u4eca\u56de\u306f\u6700\u65b0\u306e\u4ee5\u4e0b\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
    diffusers\uff1a0.25.0 transformers\uff1a4.35.2  ftfy\uff1a6.1.3 accelerate\uff1a0.26.1
    \u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u8a18\u4e8b\u3068\u5408\u308f\u305b\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n
    !pip install diffusers==0.25.0 transformers==4.35.2 ftfy==6.1.3 accelerate==0.26.1<\/code><\/pre>\n\n\n\n
    \u4ee5\u4e0b\u306e\u3088\u3046\u306b\u753b\u9762\u306b\u8868\u793a\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3001transformers\u306f\u3059\u3067\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6e08\u307f\u3067\u3057\u305f\u3002<\/p>\n\n\n\n
    Requirement already satisfied: transformers in \/usr\/local\/lib\/python3.10\/dist-packages (4.35.2)<\/code><\/pre>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \n
    \u5165\u529b\u67a0\u304c\u8868\u793a\u3055\u308c\u306a\u3044\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u753b\u50cf\u306e\u3088\u3046\u306b\u300c+\u30b3\u30fc\u30c9\u300d\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/div>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u5165\u529b\u3057\u3066\u5b9f\u884c\u3057\u307e\u3059\u3002\u30b3\u30fc\u30c9\u306fStable Diffusion\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u30c6\u30ad\u30b9\u30c8\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u57fa\u3065\u3044\u3066\u753b\u50cf\u3092\u751f\u6210\u3057\u3001\u305d\u306e\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b\u305f\u3081\u306e\u3082\u306e\u3067\u3059\u3002<\/p>\n\n\n\n
    import torch\nfrom diffusers import StableDiffusionPipeline\n\nmodel_id = "CompVis\/stable-diffusion-v1-4"\nprompt = "best quality,dog"\ndevice = "cuda"\nseed=30\n\n\npipeline = StableDiffusionPipeline.from_pretrained(model_id).to(device)\n\ngenerator = torch.Generator(device).manual_seed(seed) \nwith torch.autocast(device):\n    image = pipeline(prompt, guidance_scale=7.5, generator=generator).images[0]  \n\nimage.save("test1.png")<\/code><\/pre><\/div>\n\n\n\n
    \u30b3\u30fc\u30c9\u306e\u89e3\u8aac<\/h3>\n\n\n\n
    import torch\nfrom diffusers import StableDiffusionPipeline<\/code><\/pre><\/div>\n\n\n\n
    PyTorch\uff08torch<\/code>\uff09\u3068\u3001Diffusers\u30e9\u30a4\u30d6\u30e9\u30ea\u304b\u3089StableDiffusionPipeline\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u3044\u307e\u3059\u3002PyTorch\u306f\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u3068\u8a13\u7df4\u306b\u5e83\u304f\u4f7f\u7528\u3055\u308c\u308b\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3067\u3001Diffusers\u306f\u4e3b\u306b\u6df1\u5c64\u5b66\u7fd2\u306b\u57fa\u3065\u304f\u753b\u50cf\u751f\u6210\u306e\u305f\u3081\u306e\u30c4\u30fc\u30eb\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
    model_id = "CompVis\/stable-diffusion-v1-4"\nprompt = "best quality,dog"\ndevice = "cuda"\nseed=30<\/code><\/pre><\/div>\n\n\n\n
      \n
    • model_id<\/code>\u306f\u4f7f\u7528\u3059\u308bStable Diffusion\u30e2\u30c7\u30eb\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/li>\n\n\n\n
    • prompt<\/code>\u306f\u753b\u50cf\u751f\u6210\u306e\u305f\u3081\u306e\u30c6\u30ad\u30b9\u30c8\u6307\u793a\u3067\u3001\u3053\u3053\u3067\u306f\u54c1\u8cea\u304c\u826f\u3044\u72ac\u306e\u753b\u50cf\u3092\u8981\u6c42\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n
    • device<\/code>\u306f\u8a08\u7b97\u3092\u884c\u3046\u30c7\u30d0\u30a4\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304a\u308a\u3001\"cuda\"<\/code>\u306fNVIDIA\u306eGPU\u3092\u4f7f\u3046\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/li>\n\n\n\n
    • seed<\/code>\u306f\u4e71\u6570\u751f\u6210\u5668\u306e\u305f\u3081\u306e\u30b7\u30fc\u30c9\u5024\u3067\u3001\u753b\u50cf\u751f\u6210\u306e\u518d\u73fe\u6027\u3092\u4fdd\u8a3c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n
      pipeline = StableDiffusionPipeline.from_pretrained(model_id).to(device)<\/code><\/pre><\/div>\n\n\n\n
      \u4e8b\u524d\u8a13\u7df4\u6e08\u307f\u306eStable Diffusion\u30e2\u30c7\u30eb\u3092\u30ed\u30fc\u30c9\u3057\u3001\u6307\u5b9a\u3055\u308c\u305f\u30c7\u30d0\u30a4\u30b9\uff08\u3053\u306e\u5834\u5408\u306fGPU\uff09\u306b\u79fb\u52d5\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
      generator = torch.Generator(device).manual_seed(seed) \nwith torch.autocast(device):\n    image = pipeline(prompt, guidance_scale=7.5, generator=generator).images[0]  \n\nimage.save("test1.png")<\/code><\/pre><\/div>\n\n\n\n
        \n
      • 12\u884c\u76ee\u306f\u3001\u753b\u50cf\u751f\u6210\u306e\u969b\u306b\u4f7f\u7528\u3055\u308c\u308b\u4e71\u6570\u751f\u6210\u5668\u3092\u521d\u671f\u5316\u3057\u3001\u6307\u5b9a\u3055\u308c\u305f\u30b7\u30fc\u30c9\u5024\uff08\u3053\u306e\u5834\u5408\u306f30\uff09\u3092\u4f7f\u3063\u3066\u518d\u73fe\u6027\u3092\u78ba\u4fdd\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n
      • 13\u884c\u76ee\u306f\u3001\u30c7\u30d0\u30a4\u30b9\uff08\u3053\u3053\u3067\u306fGPU\uff09\u4e0a\u3067\u306e\u8a08\u7b97\u3092\u52b9\u7387\u5316\u3059\u308b\u305f\u3081\u306b\u81ea\u52d5\u7684\u306b\u30c7\u30fc\u30bf\u578b\u306e\u30ad\u30e3\u30b9\u30c8\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u8a08\u7b97\u901f\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u3001\u30e1\u30e2\u30ea\u4f7f\u7528\u91cf\u3092\u6e1b\u3089\u3059\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/li>\n\n\n\n
      • 14\u884c\u76ee<\/code> \u306f\u3001\u6307\u5b9a\u3055\u308c\u305f\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u57fa\u3065\u3044\u3066\u753b\u50cf\u3092\u751f\u6210\u3057\u307e\u3059\u3002guidance_scale<\/code>\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3001\u751f\u6210\u3055\u308c\u308b\u753b\u50cf\u304c\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u3069\u308c\u3060\u3051\u5bc6\u63a5\u306b\u5f93\u3046\u304b\u3092\u5236\u5fa1\u3057\u307e\u3059\uff08\u6570\u5024\u304c\u5927\u304d\u3044\u307b\u3069\u3001\u3088\u308a\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u5fe0\u5b9f\u306a\u753b\u50cf\u304c\u751f\u6210\u3055\u308c\u307e\u3059\uff09\u3002\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u306f\u30ea\u30b9\u30c8\u5f62\u5f0f\u3067\u8fd4\u3055\u308c\u308b\u305f\u3081\u3001\u6700\u521d\u306e\u8981\u7d20\uff08[0]<\/code>\uff09\u3092\u53d6\u5f97\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n
      • \u6700\u5f8c\u306b\u3001\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u3092\"test1.png\"<\/code>\u3068\u3044\u3046\u540d\u524d\u3067\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n
        \n
        \u4ee5\u4e0b\u306e\u3088\u3046\u306a\u300cNo module named ‘diffusers’\u300d\u306e\u30a8\u30e9\u30fc\u304c\u3067\u305f\u3089\u3001\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30b3\u30de\u30f3\u30c9\u3092\u518d\u5ea6\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/div>\n\n\n\n
        \"\"<\/figure>\n\n\n\n
        \u753b\u50cf\u306e\u78ba\u8a8d<\/h2>\n\n\n\n
        \u5b9f\u884c\u304c\u7d42\u4e86\u3057\u305f\u3089\u3001\u753b\u50cf\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002\u753b\u50cf\u306f\u5de6\u5074\u30e1\u30cb\u30e5\u30fc\u306e\u30d5\u30a9\u30eb\u30c0\u30a2\u30a4\u30b3\u30f3\u3092\u30af\u30ea\u30c3\u30af\u3057\u3001\u300ctest1.png\u300d\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\u3068\u753b\u9762\u53f3\u306b\u8868\u793a\u3055\u308c\u307e\u3059\u3002
        \u4eca\u56de\u306f\u30d7\u30ed\u30f3\u30d7\u30c8\u3067”best quality,dog”\u3092\u6307\u5b9a\u3057\u305f\u305f\u3081\u3001\u72ac\u306e\u753b\u50cf\u304c\u751f\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
        \"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"
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