{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "s7iq_p9RCron" }, "source": [ "# Introduction to 🤗 Diffusers\n", "\n", "![diffusers_library](https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在这个notebook里,你将训练你的第一个扩散模型来**生成美丽的蝴蝶的图片 🦋.** 在此过程中,您将了解🤗 Diffuers库,它将为我们稍后将在课程中介绍的更高级的应用程序提供良好的基础\n", "\n", "让我们开始吧!" ] }, { "cell_type": "markdown", "metadata": { "id": "s7iq_p9RCron" }, "source": [ "## 你将学习到\n", "\n", "在这个notebook中,你将:\n", "\n", "- 看到一个强大的自定义扩散模型管道 (并了解到如何制作一个自己的版本)\n", "- 通过以下方式创建您自己的迷你管道:\n", " - 回顾扩散模型背后的核心思想\n", " - 从hub中加载数据进行培训\n", " - 探索如何使用scheduler将噪声添加到数据中\n", " - 创建和训练一个UNet模型\n", " - 将各个组件拼装在一起来形成一个工作管道(working pipelines)\n", "- 编辑并运行一个脚本,用于初始化一个较长的训练,该脚本将处理\n", " - 使用Accelerate来进行多GPU加速训练\n", " - 实验日志记录以跟踪关键统计数据\n", " - 将最终的模型上传到 Hugging Face Hub\n", "\n", "❓如果您有任何问题,请发布在Hugging Face的Discord服务器 `#diffusion-models-class`频道中. 你可以在这里完成注册: https://huggingface.co/join/discord" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 预备知识\n", "\n", "在进入notebook之前,你需要:\n", "\n", "* 📖 阅读第一单元的材料\n", "* 🤗在Hugging Face Hub上创建一个账户. 你可以在这里完成注册: https://huggingface.co/join" ] }, { "cell_type": "markdown", "metadata": { "id": "zfA87mA93LLP" }, "source": [ "## 步骤 1: 设置" ] }, { "cell_type": "markdown", "metadata": { "id": "pCoVNJtLI9KT" }, "source": [ "运行以下单元以安装diffusers库以及一些其他要求:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Jw6-w4TB_7wg" }, "outputs": [], "source": [ "%pip install -qq -U diffusers datasets transformers accelerate ftfy pyarrow" ] }, { "cell_type": "markdown", "metadata": { "id": "QJfU9oqZDscp" }, "source": [ "接下来,请前往 https://huggingface.co/settings/tokens 创建具有写权限的访问令牌:" ] }, { "cell_type": "markdown", "metadata": { "id": "oa0RbEt5D9sz" }, "source": [ "![Screenshot from 2022-11-10 12-23-34.png](data:image/png;base64,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)" ] }, { "cell_type": "markdown", "metadata": { "id": "9CDekhLrETH2" }, "source": [ "您可以使用命令行来通过此令牌登录(`huggingface-cli login`)或者运行以下单元来登录:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 327 }, "id": "FlX4eeECD9HO", "outputId": "6e8d9cb7-d62b-43fb-cc09-35b8abc44b64" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "然后您需要安装Git LFS来上传模型检查点:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "!sudo apt -qq install git-lfs\n", "!git config --global credential.helper store" ] }, { "cell_type": "markdown", "metadata": { "id": "9bR764bDIaDh" }, "source": [ "最后,让我们导入将要使用的库,并定义一些方便函数,稍后我们将会在notebook中使用这些函数:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "VXhqi_hNcpk_" }, "outputs": [], "source": [ "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "from matplotlib import pyplot as plt\n", "from PIL import Image\n", "\n", "\n", "def show_images(x):\n", " \"\"\"Given a batch of images x, make a grid and convert to PIL\"\"\"\n", " x = x * 0.5 + 0.5 # Map from (-1, 1) back to (0, 1)\n", " grid = torchvision.utils.make_grid(x)\n", " grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255\n", " grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))\n", " return grid_im\n", "\n", "\n", "def make_grid(images, size=64):\n", " \"\"\"Given a list of PIL images, stack them together into a line for easy viewing\"\"\"\n", " output_im = Image.new(\"RGB\", (size * len(images), size))\n", " for i, im in enumerate(images):\n", " output_im.paste(im.resize((size, size)), (i * size, 0))\n", " return output_im\n", "\n", "\n", "# Mac users may need device = 'mps' (untested)\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "markdown", "metadata": { "id": "cir1ABwMI2yI" }, "source": [ "好了,我们都准备好了!" ] }, { "cell_type": "markdown", "metadata": { "id": "CP_mVwzHbmaa" }, "source": [ "## Dreambooth:即将到来的巅峰" ] }, { "cell_type": "markdown", "metadata": { "id": "6ttDW4giJCvY" }, "source": [ "如果你在过去几个月里看过人工智能相关的社交媒体,你就会听说过Stable Diffusion模型。这是一个强大的文本条件隐式扩散模型(别担心,我们将会学习这些概念)。但它有一个缺点:它不知道你或我长什么样,除非我们足够出名以至于互联网上发布我们的照片。\n", "\n", "Dreambooth允许我们创建自己的模型变体,并对特定的面部、对象或样式有一些额外的了解。Corridor Crew制作了一段出色的视频,用一致的任务形象来讲故事,这是一个很好的说明了这种技术的能力的例子:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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+b7vQ7qQNjTuHYhiZrZcKfUiQmMuZK0C7HcAbC1uYNzYix1Xj2R5BIG2125qje0X2UnhWItorNcsXkzWrjgBsywt1Gy6SpKtIAGW4m5Bph9lXs42b/D1xuNxFzDhle9ChMqYdRmW45YE+JQbummQp1mntjOF4XmHhuFuN4Va5avGJzW7ltsmJszKt4l7yyGOnit3IOVQV97bXP4sYe3w6yQrXlVrwXTJh0MJbEbd467fctkEQ4qFTdJsSrxBhkZLKgPcJnWGjWbnUWuRv/E22zglOp2MvIfs2081QOwPsIwnFMPfxBvYi2qYu7ZtKBak2lSzcQv4SO8i7DZTl00pif4oWD/0rE/S1/u1kfhPMmIsqVtX71pSSxW3duIpYgAsVVgJIAE7wB5VuTivEbn/owLveP3v+DbL95mbvMxtoS2ec2YnWZmpXSp2MUNRG6KrIZLIGNblHYvzJGq8puqe03bqBdJntd9njDYL8hyX77/lWOsYV84t+FLuaWWFHiEaTpUD7SnYpY4VZsXLV67dN24yEXAgACrmkZQDNUTs+5ixF7H8PW9fvXVGNwjBbl244B75BIDMQDBIn1Na39rns/wAXxCxhkwlsXGt3Xdwblu3ClMoM3GUHXoJqRUYhXYVXUVPWVILXdYZHENaCP2b32toNxdJDGSMc5reVlk/2f+Q7fEsaMNcd7am3cfMmUtKAEDxAiDNT/av2T2cHxXCYBLtx7eI/JczsEzr319rLRAC6BZEjemj7MPYzxHA8RW/ibKpaFq8hYXrL+JgAoyo7Nr5xXF7Sv/tLwz/+P/8A9dypP+0D58bdDTTB0XUOd2S1wzi+txfUaaX8knqQIruGt1UfaT7E7HC7Fi7avXbpu3WtkXAkABC0jKAZqudofY5ew+CwmPtZruHv4excu6eKxcuIpIYAa2mY+F+hIVtcrO9/b9/7Jg/+8P8A7M03OxxEPCcArhSjYLDKweCrBrSLlYNoQ05YO8x1qih6X1tLhFLWyHOXSPa8GwzN7Wmg0IsLH33TxpWOlcwaaCy/mzWi+0nsAw+F4SeIJfvNcFrC3MjC3km+9lGGihoAukjXoK5/ab7BWwRbF4RS2EJm5bElsMT+LWZ0DHVNm6MXV2/f+zDf924d/tsJV3ifSb2h1BLQydiSVrXjS9rtu1172OvDxBtYpmOntnDxqBosf9l3Z5ieI3u5wygwJuXHJW1aU6BnYAkSdAoBZtYBgxp7gPshYQIO/wAVibj9TZFq0nwCul5tNpLCd4ExVo7EsHa4VwFcUVzM2HONukaG41xc9lJMx4DbtDpJZo8RrJfHOe+J8TxIHfXrl2435qxZZkRdyFtWlaBA6mWMSzMdaZfX4pjdTM2imEEERLS+1y4jc67Ab7iwIvfgoMjhaM4uTwTX7VvZVu2Lb3sFeN9UBZrN0Kl0KNWKXBFtyBrlItmAYzGAc01oINzKcFdwL4fF3LV2FL3EZrypJL21uls2R9FYNm8IKjKGIKT5q5axGFuC3ibT2bhUOEuDKxUkgNHkSpHyNaLo5NVBjoquojlcD2Swi5bzcB392nEm6jzht7taR4qJrUP/AOH7/LY//wCFY/8AvesvU7fZQ7TMLw25imxPeRdS0qd2mfVWYmfEI3FK6XUstThM0ULS5xAsBue00/BFM4NkBKgvar/9dY7+na/2FmtRex/dy8EtsN1fFETtIdjr6aVkPt05mtYziOJxNnN3V1kKZxlbw2raGVkxqp606+wPtxwOC4WMLfN7vQcQfBbDL+cLFfFmHnrpWd6S4VVVGBU1PHGXPb1WZo3FmEG/gd0/TyNbM5xPNQf+NzxL/MYH/wAPE/8AmqdvtC8Sa9y1dvOFD3sPw+64UEKGuX8I7BQSzBZYwCzGIknesG1pztH7bcDf4F+QWze/KPyfA2vFbhM1h8O1zx5jpFpoMa6edGNdF4YauikoIALTNLy0bNBBue4IiqCWuD3cNE0fYh/9Uj/vN/8AVbrGPJfKOKxjsuFs3LzIpdsg90AEyWMAEwQonMx0UEwK0F7NPbhgeH4AYfEG93nfXX8FvMuVska5hroelUr2bu3P/BYexettdw1x+8Pd5e9tvlCllDEK6sFUFGZYPiBHiDe0VPiVFU4lUQQZnOcwsDjYOAz3I52B2uL80PdG9sbSdr37tlM9h/tCY6xftYXFZ8Vaa4lmHBOJtlmCeFvfuEE/ydzMxgAMtXr29eVbXcYfGhQt7vhh3YADvEa3cuLm6k2zahT5ORqAsXR/aN4KJui4xuR0w1zvT6ZyoX09+PWs1e0b2ztxV7aIhtYaySbaMQXdzoblzKSoIHhVATlBbxHNpW4XR1VXjMVZFRmma3N1hJsH3GwFm315DXc6gJyRzWxFhdm5dyUNbs9m/GW+I8B/JC0MtrEYK9l3QOHFtwDv+auKQdiyuOhAwnVz7I+0fEcMxHfWDKsAt60093dSZho1DKdVcaqZ3DMrbHpXgsmJ0eSE2kY4PYf4hfTzv77X0UWmlEbrnY6FRnP3J2IwN9rGJtlHUmDByXF6PbeAHQ+Y22IBBA+sFyPi3wtzGrYc4a2VV7sQupKyswXCtCsVBCkrMTWwuBe03wq/bH5Styywgm3dtG8maPsNbDyOksiH0FVftg9qDDGxcw+CtG8biNbZ79sLYVGBVgLLeK4cpIyuqIJE5wCppqfpFjkj2QGgIdcZ3E2Zb9og2ttt2nW5OTroIQCc/hzV29j+7l4JbYbq+KInaQ7HX00pJf43PEv8xgf/AA8T/wCaqc7A+3HA4LhYwt83u9BxB8FsMv5wsV8WYeeulZeqPhHRiKfEK2SvguDJdhcNwS65HovZaghjAx3DVby9oXiTXuWrt5woe9h+H3XCghQ1y/hHYKCWYLLGAWYxEk70pvYU5G7y/dx7jw2Js2Z63nWbjD1t2mA/+cCNq++0fttwN/gX5BbN78o/J8Da8VuEzWHw7XPHmOkWmgxrp51IcA7d+H4HhIwmCN44lLBVGa0FU4i5q90ku2iuzOqkHRVXaqimw3EqfB5qKCFwfLO4DSwbGQwFx7iBl8L8k66SN0oe47D1TO525P4pc4xhsbZfDjDYYLbFtrtwM9q5/wBpJUWigdwSFliB3VljqIqr+3HyH32Ft462s3MKcl2Bq2HdtCdCT3VwyBoAt26TtWe/+n3i/wDptz+pZ/uqcfZn7R+GfAvh+Km7duN3tt2W2rC7ZuD7WUoFYBmtwBsqGZJoPR7GcNlpqtrWP6mzMsYdmcw3ve4AO515m9uR18Ugc3UX115qc9gP/seL/wC8r/skqhdn/bg/DuJY2xfLXME+NxUjdsOxv3JuWx1UnV7Y31ZfFIf79mjtdwHDLWLtXWvMHxJeyy2gS1oKEVmGcZWIEldYpC88cRW9isTdScl2/fuJIg5XuM6yOhgiRV3S9H/a8Tr/AGuI9VII8pItew3aeBCadNljZlOouv6bcvNZZe+sZCmIIvG5biLpKoguEj3iUtos7woB2rNHsRczKL/EcGxAdrn5RaH3grNbuidpE2yBvGc7KYV3s39ttzhtwWr2a5gnaXTdrJJ1uWv1tb2bcQ26ywnHrljFflGHuNbuJda5auLuNTGhEEEGGVgQykqwIJFVtB0GmjjraKR12yCPq3/3S4gHlbQHuOiW+sBLHAbXuEyfan7Nb+Ex1+/kZsNibr3rd0AlVa6xd7TkCEZXZgob3kykEnMAyuw3t8xd04Lh1jApcNu3Ysm53j+G3aVLTXnASFVQMx1iYAkkTP8AZ97VuFuIEx1p7NyId7a97YfaTlk3Un7mW4BHvdKl+Oe01wnD2z+TK95jslqybCkwYzvcVIHSVRyJ2NR6yTE6imZQ1uHGV7BZr8xDL2sHG2m2/aF/4Upoja4vY+wPDiub27uNKnD7NiRnvYhWA693aRy7D4M9sfpVc+eebbuB4EuKshGuWsNgcouBihzth7RkKyMfC5IhhrG+1Yg7WO0C/wASxJxF8gaZbdtZyWrYJIRZ1OpJLHViSdBAGreV/aY4Xbw9i25xGa3ZtI0WZGZEVTBz6iRTOI9F6miw+jhbCZiyQvka3Y3sS2/Kwy3sea9ZUNc95va4sElOafac4hibF7DvawgS9buWnK27wYLcUoxUnEMAYOhII9DSv5M5SxeKZ/yWzdutZXvXNsGUCywM6eM5TkQeNyIUMdK2YvtRcJ//AFH/AIA/36SnYN7QAwDXrN621zC3L1y6jWwou2i518JIV1YAeEsCpkgn3a0OF1VZBSTex4d1ThlIaXaPvcG3ZGrQBpfW/kWJGtLhmffv5Lu7AfaDxqYixhMVmxVu5cSyCQTibZYhAc+90KTLLcBc9HEQbN7evKlkJhsaqhbzXO4uEad4uRnRm82t5CobfKwBkKsX277RvBVm6rsbkbLhnF0+mdlVZO2rx61mP2ie2FuK3beVDaw9nN3VsmWZmjNcuR4cxAACiQgkAnMSavCaKqqsZjrIqQ0zWh3WXNg+4OgFm8eQ7zqAnJXNbEWF2bl3JU1vP2PLkcEtkbh8UR8nY1gytUezn264DAcOt4bEG93iveY5LWZYdyw1zDp6Vf8AT6gnrMObHAwvIkabDkA5M0T2tfdxtoqyPa24l/mcF/4V/wD8zSJ41j2u3bl1oDXHe4wGgDOxcwCSYk6STW3/APGh4T/+o/8AAH+/WUPaA5ps43iWJxNjN3VzuMmZcreCxZtNKyY8SHrtFI6KuLZ3sGHmnBbcuvfMQRZv2RzJ34L2p2HbzLQ3sN85C9hr/DrsN3Wa5aVohrF0xdSPJbjZjMz356Cuv20ObVwmBscOsQnfBQVXTu8LYyqqASCA7qqjcFbV1TvWXuyHnBsBjsPiRMW3AuqPtWW8F1YkAkoSVnQMFPSuztz52PEMffxAJ7stksAzpZTw29DqC2twjoztTD+iV+kArLfVW6wjh1o02/8Anfnde+0/U5eO3l86KR9mD/1xgP8A4rf7O5Wovak7X8TwtsKMOlhxeW8X75bjR3ZthcuS7bj3zMz0rI3YnzFbwnEcLib2bu7TlnyjM0FGXQSJ1I61rpvaj4T54j/wB/v1B6X0E78WhqBSmeNsZBbwJu7jY7XB2S6V7RGW5rG6zB2udtWL4nat2sQmHVbb94pspcUlspTUvduCIPQCtR+z9imTlm26HK6YbiLowiVZb+LZTrI0IB1EVQe3zt64djeG4nDWO+7273OTNaCr4L9m60tmMeFD03iozsx7b8Dh+CDA3De78WMbb8NuUzXrmIdPFmGkXVkxpr5UziVFUV2EwxRURiyztPV79nKSX7DQl1jovWPayQkvvpun92Wc+nifDu/w7JaxORrbhlzLZxIXdkmTbJIuLr7jAHUMKwB2hDEjF4gYwucSLjC+X3L9dvDliMuXw5MuXwxVt9nftObhmLFxszYe6AmJQakqJKuokDPbJJE7qbi6ZpFr9qDnXhfETbxGFN5cUsJcz2sq3bWuUkhj47Z0BjxKYJ8CCrPA8HlwTFZIo4s0EurXgXMZ/cJ3y/8A5PNNzSiWMEnUcOfemH/+H3jXNviNssciPhXVege4uIV2Hqws2wf6ApBe0Li2fi3EC5LEYm6gJ6JbPdIvwVEVR6AVfPZM7VMJwz8t/Ku8/P8A5N3fdpn/AJL8ozT4lj+VWPPXypV9qPGUxGOxd+3Pd3sReuJmENldyyyNYMHaanYZh00fSCrqXRkMc1ga62hNmXt5jXvCRJIDC1t9dVW63fxf/wBlB/8Atdn/AGSVhCtRcQ7ccC3AhgAb35R+Q28P/JjJ3ioqnx5vdkHWKOmFBUVTqTqWF2WZjnW4AcT3L2le1ua54JB9k/8A6wwH/fMJ/trdbb9p7tQxHC7OHuYdLLm7ddGF5bjAALmBXu7tsgz5k1hjkPiS2cXhbzzktYixceBJy27iu0DSTAMCnb7Vna9g+JWMNbw3e5rV13bvLeQQUyiDmMmaY6R4M6vxajL4s8QD8+mguNL+a9glyROsbHSyYfs8dv2M4jjlw161hUQ27rzaS8HlACBL37ixrr4fpVZ9pX/2l4Z//H//AOu5Sn9mvnSxgMeuIxGfuxaup4FzNLgAaSNNPOrH2y9puFxfGcFjbXedxY/JO8zJlf8AM4h7rwuYz4WEaiTpUNnR72TG3PpYcsXUOFwNMxvp4nRK6/NFZx1um37fv/ZMH/3h/wDZmrLxf/2UH/7XZ/2SUmfar7X8HxLD4e3hu9zW7zO3eW8gylCuhzGTNTPEO3HAtwIYAG9+UfkNvD/yYyd4qKp8eb3ZB1iqSDA64YXQRGJ2Zk+Zwtq1uYm57k86ZnWPN9wp32Ye3oXwuA4gwNwju7F+5BF8EZRZvltDcI8Ku2l33W8cG6w/avwypwLFooCqi4RVUbKq4rDqoHoAAK/n7TtvduL4jg2K4fiyz3suH/J7+5uLbxFlzbvHfOqISLh94CG8UF7jFuheTEoK6iFm9bG6Rg2HaF3tHxHDcaaBqOrvGWP5Gx/JaK7MMnFeXUw6sFY4X8jade7vWEFtCwGseG3djco42msfcGfEcI4hae/YIvYZ83dPKhxDKCHAIZDqQ6ypjQmursV7VsRwu8XtRctXI76wxIVwNiGElLgkw4B3IIYaVqnhftL8IvoO/wC8tEf5O9YN0A/zTbFxSPUhT6Cmn0uIYHPUMipzPTzEus09ppdoRoCbW022ANwbhehzJg27rOCk/Z77WMVxRrrPg1sYe2ul4O7B7pYAW0zIqtChmYgnL4AffFZv9tPjK3eLOqme4s2rDEbZxnusP0TdynyKkdKanaf7VVhLRt8Ots1wjKt64gS1bH3ktE53YdA4RQYJDgFTkXGYlnZndizsxZmYkszMZZmY6kkkkk6kmneiHR6SKtfiD4OoaW5WR3JOtruN9eHG2+wtr5UzgsDAb8yn9/0G4X/O3/rb/u6P+g3C/wCdv/W3/d01K7+A8NN24EkKNSzHQIo1ZifID8Yr6CmoaOJhke0AAXO6+fabpDjFRK2KKVxc4gAWGpPkl/yV7M2Gvks9+/asp79wm1qfupNuCepOyj4gVz8xdh3CEJW1isVcI/n4fX4AWhTd5n7XsJhU7jC2lu5d713QM2xZVys7ehOQAREjWkHzx2gm80lVknQIG1npBGafIg1yytxV08x6gZW8APzJX0PguBSQU4FU4vfxJt6AbDlfVTeH7A+HtaW4t/FamCC1nSDG3czVH4r2Y4VLAud5fNxrjBBNvKEXcn83JJkbEU/Ozfs94m2GzPhyik57a3HUXSrQfFbnMvmA0NqRlpcc5cr4pfzZs3PCWG3mZ/iaYFRUjcn3KzFHTHQAG3f/ADSOx/L6AkIWIG5YiPgAAKlezjlCxiMUlm7cZVeVBRkDZzoohlaQTvpU/wAW5GxZBAtwCZMkfTSaheUeAXrGOwjMsBcTYJIPQXFn12qwZJIWG7rGyjy08TSA1l9e9WnmbsisW/5O5eYyogm3sRJ2Qdajcb2VC2oa4L6qftHLl+uSKsfapzG1tQV3zjoSPdJMgEabdauPYz2i28QhsuAGjUfZdeuhH1DZo31pttRJ1YfuoxpmZy1L3lvsqwl3Q3b4b0NuCPT8308qmv8AoNwv+dv/AFt/3dSnNdoYa+Gt+4WkL0HXTy6iNtCNtrzgMWrqGUyCP+Y+VbLAfZ6phZI0Zh6jn5cVyrpvJiOGzNmp5XCN2ltOy4cL22I1Hmll/wBBuF/zuI+tv+7o/wCg3C/52/8AW3/d01KK0H9F0v7g9VhP9qcU/wCcfcP0S54V2A4R803sQIjY2us/+79K7v8AF0wf+exP1tf3VM/lz7f6P9qpiuE9MsUqqPF5oIHlrBksBbS7Gk7jmSvpToBTR1+BwVFSMz3Z7uO5tI8Daw0AASY/xdMH/nsT9bX91X6PZzwf+exP1tf3VNniuOyCdPixhR6aAsx/mqD0kqCDSo5v5oDkhrjEfdVWy/1ZI+epqvoqjFqkZutIHeBr4Cy001Hh8Zt1YJ8/1Xx/i6YP/PYn+ta/uqg+cOw3DWVXLcxDEzubcaR5WxvP4VDYjHWf6P8AStlR9SsV1DGvkhbmZD0zNl+TIQy/okGriEV0bw6SYkcsoChyQUbm2ZGAfFRPDOzDDkM1x76ohAY+CWO4t2wU1cjUk+FAQTMqrRGK5HsLrnu+iyhPoCcgn4wPgKtV7jByqjKVVRC5ZZfMkmS8sZJLZiTqTUZdeTII9Dv+GmlThVTX1cVHNFT20YFEWOz+2d2ua9JXT55f2V4Hkuz966PmhPz8Aj4AmrJ/hEgaqSf/AHfiH0MEfjXDwxbjFydATIUgEjp1kDby3p/2mW32kz7HDf7CiRybY+/c/rKP12/217jkC0RKvcPzWR8Vy/iCfwrtxuMK+8kDzZco/r28yj9ICvzCcShh3YJJgRI0hgT1g6bEbh/jSmyzb5tPJNvp4Bs0K8cqdh+BxFlLq3cUA0gqWtSrKxR1nuhIDKYMCRB0mKlP8XXB/wCexP1tf3VWLkbm7DWbKW2NxDLMxa05Ge4xdo7sPCgtAmNAJ61bMBzPh3IC3kk7AnIx+CvlJ+QrI1WI4kyR2VzstzbTh42VvHh9G5gu0Xtz/mlj/i6YP/PYn62v7qodewrC/wCdxH1t/wB3T/qrJW8+j+R1f1/tJzZertfhfPfbnYLlH0oVUuGGm9jdkzdZmtxt1dt77XPvSrHYRhf87iPrb/u6neT/AGccFedle/iRAtkZWsyc123aO9o7d4PmRV8WvQYd3V1t5szI6EpmJAdSp9zxAiZBGqsFYaqK3VfhcJgd1YDXW0Pf56a7LnGFdKK4VTOukc5l9RYbbX0F9N9OS5D7IPD/APSMZ/Wsf3FfD+yJw4Ak4nGADUkvYAAG5J7iAKs/AuP8ZAVHSyonL32NQWtdh4u8ttcnoVtOx6yZpncKwbImfE3u+ZfGWKLatW4E/m7SiYESHuG5cmYIBCjnLnzRntO8vkLrZebXBSEX2SuHn3b+NI+8WsKv1NjMR5MqspkQfLmx/sf4Ygd3i79s9c4t3QfhlSzl+jVpbDFj4m8I3CH3h63D0P8ANGi6yW6K/mftmtpd7uxbF0AwbrNCE7eBRqy/zswnoCIJehlmebNSOsdzS3xvsk4QZIv4rUwzZ7BAOgGhtIQWOgBJEwM0kT04T2UOHMzJ+UY7MkZvFhiASJALLaZQ0a5Cc0EGIIJcHJHPa4lu6uIEL5lVlYlWInMkEBkaAY94GI0OUH3fiIweGsoIe8652kmC7eO7cc+8QWaBqCfMBTUkPeRY7pGZ4O5Sgv8AsfYGPDicXP8AONmD8xZkfQ1F3/ZPwamGvYsH+lZg+oPc6/rH1jRnJvHvyi2zZcrqQGGoBBEqy5tQGgwD1U6xrXdbsBpBUMB7wBZLik+aljM6+JXEj3Q29SIJshtILpicSuHYeQfRZmt+yngf9Ixf9az/AHNdNv2S8B/pGL/rWP7mtEnAgzkmV95DuB5r5j4iQQQdRFFhatGtheLtCrTPVxmz3FZ+T2ReH/6RjP61j+4rk4r7KvDbaknE4zT+dY/uKf8AzBxMW1JPSq9f4MzupxFxFtnXus3iY/dYmAAJ8WWfKRvVJjOLUeGsBmOpvlaAS55HBoHlyAuLkK3w9lRUOsDfn3JE2PZnwAti5cxGKHeH80oayDl+8xNmPFuAOkeuXiT2d+HllUYjESxgTcw4n3dpsgNoxJg7oV3OjUfh97F4o3mzW7YU27YmAqZjmJBEAlFG+oDXBs7KzI4ZftWUW0Skt9nRc2vvC2qM7kndigBJAzHSs1h1VVvYZJ32LiTbSzeTRzAGl9yblbA0cUbQCLlI3B+yPgGGZcVimB6BrOYHy1swdZA0AOkb1+Yz2UOHCIxGMJOyl8PJjcD8xvGo8/pL34jw0MBcRHVtcrKrWmkjK3v5AwIA8LoyNlQwcoIpnN+LvkHNbJI0LoQzNHunJadri3FOoyMZ8QGQlSLGWuewanVNso2OOgSHxXs64W2wL3sQ9otAuW2tDRsotoyG2xS4zMAZlI1BINTPJns0cPxDlfyjFgCPFNkT4cx07kwQZUrPhKMTOZakOJ8Sv95muB18LZny5VuLq5R5CkOYUSVVtYMFhmtvZJxcqzMZV7qM6DWQugMqBAEIrKoE5QCPCwFRxXTXvfRPuoYrHTVUDi/sz4FXypfxZE3AstZk5QAp/kQADcJWToAupBZQe/iPss4C2PFicUX3Kq1mQJ8u50P2dYlpiYAa38T5lCXg2y22uQNJCq10mIOWL18ZiDrlQSDmFevDuItla/fYjM3gTqPDmluqsUZSFAhVuLP5xmWvZcQkBIYfNEdAw2uEl8d2BYZWZRdvkrJbxWoUaxmbu4+LGBCt6hfPD9ieAKFu/wATMCBNoEklxoptSAMhMkgEK3iABZWNj71xl8g75iB0A26bAxqQScrrJQWwtav8uXWkySC0mJA0GVVA6AISoAgKogRvTIrZ9LvPopv9FscNGD1UT/0CYLIGN7E5iufKHsnKupUEmyoLNpC6aSxgBstI4p2bYRWCi+4JJABYFj5QFsD8Y+FMTifG2Xu0ZQwAZnBgqCJPjGVs+wVEXKxhDKEIz2M8XXuw1p3c6F+7J7tXIPvXWGXLtAV3AMkMw1pXtc4IOY28lHdRRC4y2Sdbstw4y+O+ZAzOChUEyR7ttvDljXNoQ8wNpflrso4ex/P3sSEj3rbWTlIBOoNozMHQHMPIxVyJlcxTXXWEbQ6a5UMEjch1mdYiagsbxrui7sUDEwxQHLBgfnUt+AFh4VBB33UgUSzzyNLWvIvy38tE17NCBq1Tr+zbgIW6uKxF3DuYW4htAqZ0VwbRjynTXQhZE2bh3sm8NcAjE4z+tY/uKgeyntEt27hRsjYe6Mt62qhV9LihVXVdZhRIk7wQ9bOJXDtayOWtXgTbJ1iMpjP1BDaTroZmJr3B8YfHOKCtuXuv1b7WDwBch1tA9oB4AOGosdFSYjRvYOsiPZ+CV1cHOXFjawxVdDecqx65EGaPgzHX+iKkbSEkACSSAANyToAPjUZ27YK3Zs2LefNflmuKCCBMaD4bE9fwrovTKqEdF1QOriNOYGp/Jcp+i3D3T4t15bdsbXG/AOIsPPdIPi9+5duBEMTOvoNyT5D91aJ9jfslR7z42+DcXDsLdgPqvfxme5GxNsFQu4DNO60puzvh6m+7nZUZj/RRWusPn3YHzranYbbTD8JwruwVXt/lDs2n8uxvCfWHCgegFYGkILg3gBc/zXfMRlcyI23JDR56n9PNXviGLCjUgerMFH7T9BVJ5nS3c1OUnzCXG/1swn6VUecO222rFcLh3vsNDcjKPlCtcPzyV5cK5wu37Qe4jWnkgqSx22YTqAfI+VSnTiV+hUGloZIgHOFvPX3KP41w1NdB/UcUq+dOEKGDKBKkEQdZBnYirp2jcfvoi/k65mZjmJGbKANNJ6+Z8vWlxxTmrGkRcwyuPRWB/Ww/CnHOBFipga4apa84Ws8giYJ06/EetV3s+sMuNt5fMkn+aNTPrpHrIq48fvLcmFKON0bRh6jYkfxFQXKDZTduHf3AfIaFv2fSm2Hq2Fp2UaaHO7MFYe0vjMlQDrP6p/XrVp7H8fmS4s7ZWA+Mg/qFKDG3jeNxhJg6D0X94k1N8l8We0wKTO20gg7gjqKtMMqPZpmynYb+B0WY6RYYcQopKZpAJtYnmCCPfa3mtAUVWsPzTHdi4oVnKqANTqYBI6D5zVlroVJXQ1TS6I3tvwXAMVwWqwx4ZUttcXGoII8lL8ufb/R/tV1cZ4olpczddANJMCTqdAANSx0A+h5eXPt/o/2qXfa9x7u75J1Fu2mRTsXYlwSOo0DEbHulB3rg/SqkFT0knY7bsE/+2xfTn0eTGLovTOG/1n4si6efebgADdcWwRKW1EuV6HIfdU7h7urakKtKviPO1snTMB8VJ/YKo/H+KvddmZiSxJJJkknqTUNcar+CiAA+CnzVRGqZFnmFW2usPiDH+q8f6teN/XVcpb71tsjH+kpChvmCfWqZy7wF77HJssZm10J2AjcmCemgPpNntclYoe7DfEEfvpExhhdlLwDyKjsq77jz3Xm2NdTqCPqPwoHFD1mpXD8m4/pYZ/6Lr+pyv4V14fkPHsYGCvE/G2B9S2X8aiGppv32f9zf1Uj2pvNQ9nGT1/A113eIogA0LGN9SSegUfSNTTE5R7BMddIN4LhU6ie+u/ABYw4kdTdMaeE6inxyD2T4bCDwIA/2rrRcvvtOa6RCAx7lsBPSZNZ/EulGH0Y0cHu5NP56+gKPaHO+wLrHWMxl1SAbT28xhTcRrakn+kBIG502qUtKLI8BUt9qAMp/on3h6T+G1bH5y5aw96y1i7aV7b7/AHwRs6Pqyup1VhsehEisZc5cIfCYi7h3OY22GV4gXLTgPbeNpKkZgJCuHWTlqXgWPx4m0ta3K4a2ve45+XEd48kB7g6zt10Yfj2b4+RrttcSHUSKpeIWTIrswl8+f1rQmEWuErOU2OTucXsRJNywIzIdWtjzt9YH3NjsACZphKKz/hMSRr6H4furQNarodCxjp3NFicl++2dck+lR5cKUE7db/413YRAqh2AaSRbQyAxWMz3CCG7tZACqQbjEgFQjmvfAcDxeMbKMTfS2p8S2zbsYa2NPD3dq2CxjUKxdzIlgDmHXyjwM4m5btzCpbY3GG4QXrj5VmfETdABOihs2sBTcecOf8HgE7oQ9xAYw9oglepN59RbmcxLk3GksFczTWOV7GvLMueW53+zGLkCw2uQLnmTc3FgoHRjDJ3ASMcWR2btbNIbAuJduGgmwsdANLG5PZyZyNhcCCyLmvNo1+4A19ydcikABQY9y2ACFlpILVYr9jNlzRCsHy7ywI7uTscreP8Aprbg6axXJpumyuIxRVbtxTcKjwphrTDOtoSSZVIa7cYyz5tkS2iV887K+LFuStrDYe7jMSxnRsqLasx1NtL2YgTN1V2ZDWTyvc466jj+i3hF1w9v3NfcWO5Uw91Zf0tTkA/+a3h9US6PKkJw58xQjVgTIGuaII0Hn7vxim9yEHxuNuXX0zCSN+7Q+FVHTS2rJtqzEkeI0+rFsKIGgH8fEn1Opq6o2BjLJMrQ0DmVlvku6VAYHxC8Cp65h3ZWPM5htVl4pfvXxnNu4W7u3bgW38IUZTPh0BOdtfvU+l4emfvMi95GXPAzRMxm33rrFSMgvdIz9yWHY+DnxIIIEWYkEaKboB1jeSfnTENnUHqNj6eR9D5fA7ioLi2L7i5caNCtpo/mm6iXI9fExHrFSlvia993WmqKynzJzEr/AFQGH6XpUWUHNdOZV03LAJBHvp19CJKt/NYfQwRqK4OJoFM9GE/PqP4869OOkoVur08Dj7ymSJ+DSAehf4ioHtB40q2wwOhBI+cSI85Go8yak0ry1w5KPUwhzFV+K3xexNu1lzrJNwdAigmW9M0COsx1r342me6Ga0pCHJbOaWYzC5VUwurfb89jOhgn/JbZZhmv3MrOPU6JbH9GT88x6gVGcV4ucpYxLBUAEZQpDl4AI1IRtNJzLqBqOe1NS3E651af6tgyRa7i5zSW2GbQN5tAJ0stZhFG6ni7z82XjzrzEbYW1YyflDgi3JY20gZnvPlB8FtfHqAGyjroOLkhsPhFOIuPnd/5TF3zL3GEBhbEgIoJyQsNMLL5FATHMfP1sYu6jNla46WMwkMllHm6QY0a7cVmnbwWmC+OKp/a3zbiMReDKLtvDImSyiZxbAAKKRlOVsseEzqVLAjNVtFBI5w1tfj3fPDx4CynSFrWmwvbdaqxvbFw5lJjORoA+TvHOhOS3cPe5QDmnwiNppecV5vs4tm7q2jxIMqtu6vwKgXddBmW4RoIG+XLHdm4FABVVYrbU65VzMxJgAEjNq0CT5dLJhbT2AFLGSC1sj3kZTETM5WhhvIOVhsZlVFED+0fRMU8padQnva4s9tSWctbWJzGXtqZg5/de0Ng3vDZsxBYRNrmcrdzqZ/lB4TMZgW0AMqFYAAajKd4ilv/AIQuuMxJLQATvmBGs6bmYkRIgnzq68o8puyzEajcGNA0adfn1IqC6LK2wVpDG+ThopPH3zcIeJjX0YgDLPSC05piVLakxU7wm01zJI8KA7zJLnM5J08TMoPoMoGigCz8ucsKEh9TuNNPhUqnDAkhRv8AECgRkDVTmRsab8lFYPBADXoIG22npXQloawP2+mv8Guu4ka/h+H8fCv17Pl6Uktun84VK5y5dDroIO86a6aDSDAI66xtvqteS+JXBfFm7cVbSEnxa20Xdu7SVUOxjxqGYk+5caCNAYqyCBoD8twdf20qO0vklC+aWQbwsw0E6npOs6+kRUiMDZyg1cfWNzDcKQu8NtmburgjQ3LaqxETBa4SwgeKRuJmPdqh8xLYzlkLoQHYsjRGVS0fZ96Mg0ytI1IM1z4jBYZEJv4i7lAjIhOQmZXNlZ3YSZVAO6mWKjY8WG49hQJH5RcAzCMilAoAj3ske8W8IHunTeJEdPbVtyqeSbg6wUbwXCoXVrNwEyCMsJrvpBIRgYIIywdgOj47M+YnULh8T4rN14AYEPbY5oe2zSQwIAZSSSzAqZzB0OqWGuZ7BCt1UsCrDSVPiLqfImTMa7imVb4q72VIJYsBmBjx5QBDbZb1vbOupkEZQ+UV+KUbZ22dfTUHYtPAg8CDsQiJrXNINvyVlx/HDh1zqJue7bG/iM6x6CfrSv5vvOGZrxJuNrqZOutNLF2cwIBKnowiQfMSDSt7QOXblsG4WzqZltS0wTqDO4B1np0radLsNqXVHtJF47AX/d4Wt3nj3rAfRbjeHikFBfLMS42I+3xuDto0WsddFB8O4x3du8Zgm06j4scgHzzVsvm/FLbwmERoK28PYCqfcBFtVzFdiY8ImQBMb1/PvDXGe9aDaL31oR0Euo1+tf05PLYz4a4ylhaVTliYZbcIY/mt4h5MFPSqBlOWac9+5dGqapgkDiNr27zZIrjRe1kN1Wsi5Pdi4ptloiYVoI3A1A3HnUrgLTMsidKrXtE8h3sXxRcRkxMLbQWSiE28oEPbYlSqnPnYglZVx8Q8+S+CHC8OtJeA70opuzBIYj3SwkHIISQSCQSN6s5qNkbWvY+9xcjy9NdNeSg02NSSudHJFY3sN7HU899NdDxtoknxm4QY/D9gG5quc24kYZraX7lqzcugG3ZuMxulSSA5t2rdwopIIGfKSQQASCAy8NcVb4uQDlJj0J0n4jWlD24dnjY3HPiVzaraCsty2sBEVcpW4ZBDKWBUAQ2smYepqaJ2sh8r2ScQxCpZ2YWHxtm18OXNR3M+HD+F1EwHQggyp917bjcHUee4YDUUusThwpZdgTM7CSIP7Pxpuf4BZbFlH1e33pJME/nGzFZXwmdGaNM21LznHhujD41EqYmB5a03bfQqdSSvmga+VuV9tQqrw7hly08rBB3B/WDUu/MvdyAEVupEFvw1pULjHAgOwHkGYD6TFSPB7gO+9LfG5ovdVMcjJHWsnh2U4e3eY3nfNdU6Id18mjr6RoPjTNrNOAxTKV7s+P7JGhB+P8CtIYJiUUkySqyRsTA1HxrZ9G6ljojE1ti3c87/AJrjX0jYbLFUtqXyZg+4DT+xa2g4W1vwN+e6neXPt/o/2qUHtO8KdWtXwCbbL3b+SuJKk/01MD1Q+Ypv8ufb/R/tVIcSwKXEa3cVXRhDKwkEeoP19DBG1cf6WVvsnSSaW1x2AR3GNn+q7V9HUHXdF6dnH6y3/uyLC2IrkNosYEkkwANSSdgBuSdorSHMPs+WnebOIa0mpyPb77L6K/eW2y/0sx9TXby72DYO0M183cURqUU9yp/oqjZ58pu1aM6S0FhZ+ptpY6ePDTjqrCbCKp1xl08R+t/RXjsq7CblmxbS4FQwGusTmZrjAFoVei6IMxUwokSTTJw3ZlZX7TH5KP2GlPx3tHbDFDhsTesBp/MY7vbqNGkKMWBi1ynQ5L+UeVHJfbg97GWVx17B4axYF28XtZ1F+5kOHS0e8uvlXLfe7G5NpdR15zjOA4zI984ma5ti7sg5jubBtjubAWJAumhFkboNB6eKdnDuV8Os6ZiI94+euwyj6ip7D2ABCqAPJRA/DSkH229ploi3d4ZjW773L3cIt+29kZnBctau2ke2zEqQVbLcujXTLTuK8scYuYZsVew2IxlvwEIbi4h3zFQGtYVHuZVE5iFRYEsVgE1BwzoTVYgwSTSlt/2S0lwPHQ5Rbkb7I6xjRckAfPBajvcTtAwbtoN5G4gP0LTX040kajzGo+u1JPs89nK7i7Nq9jLpwq3FzHCpYUX7YPuh7l0lEbLBKmxKzlMFTTa4J7PnDLCxat37baTct4vFW7hI65rV1APgAB6VeO+i+4u2ex/ibe/uOnqmZMQiZ9kl3lb4n8ly8Yasq+1NYjFYd/vYbKf/AJd+8f1XQPkK1PzVwIYdcoxN3ESxEXu7draxt3qIjnUf5Y3LhzbwNMv+08hJwjdIxFsnyM2HUfMFj+ia9wHDX4digp5HNJsR2TcbE9xB02ISGzCQhwSzWwu5NfFpPKotrhmuqziDtFdJyHcJ4rpbEFQT6GR51p6st4i2SD8D+qtS/wAfx+r5itf0Xt9b93/MuS/Sfr7N/i/+NVLtPvd2MOyyGYYgMVJUlQbICkjUqDmMfzjS+XGFitsBVVmVSoGhDEAg9IiZECRI1pq808sNiu7S2T3i58ihS2bPkzAhdQPCpzbLrO8jg5j7JrmBwtvEYh1N58Rbt93bM27SFLzktcIBdybaDwhVXUAvIaqjGIupq3F9ruNxrr7txbvHgrjorWxT4dGyO/ZGV2h38djffQ6X1srFf7RL1zBYjD3WNx7rqouGM3cXM7X0YQAV8ItjT3b5XQIKrOGxrZbusm/kFxiSWYd6t5gTvLFAWJO4bzqHvXQoJO3X9wHr7vz9KtXZVwA43EW1Ii3Ie4Nz3Y7wR8XB+ESN4qpjZyWkLRdPTsJ4B3WGF1h48Qc/qLe1sfpD85+mB0phAVGXOPYZCEN+wpA0Q3bakAQPdLTA0H0rutY1CCwdCoElgwIA3mQTVi0ZRYKA+7jde4FfQFcP+ER5H3C2WPGMoRnBE6kC4vhEn3t9BXzj+P2Lfv3rSnyLrmPwUEsfkKUgMKiO0bDfm846eBv6JZHH0dF+RNUzD3TKmTICgHqMghI+AH4CmR+XW7yMuW7kYESbN0Ag9VlJI+XSl7iMEbbFW84B+hEzEGIMEDr8A0/e6kxjRW/D8Y75DbKwzI0GdC6jMsCNJInfQiNZmqVYAuXrKtqoZ7gHqokfRob61JWLsa7Eaj0I1B+tRfCsWHxZddAqXHuDorHKv0bf5E+Yqk6QyGLDJy02ORwB7yLfnopEEd5GjvCrVzi7XbjtchclxlVIbRhoJlVBM6HLmj7LGDHHzJiQyAjfxx8RbygiOviH6q7MdZzFkRNBP5xveMAjKB4fEEYgwARmGbMrjPSufOKBLaxEozTB3MqxMa/dA116bVQmNrbRjYAAAchotgzRtws5doT97iQw91lRgw+7rJB8t4naQNNhaeBtaf3lmdpEwIjWd9v1V9cvcMW/YFhR+eOIYA7AWe7UiTuAuUknooY61oDsv7LrHdKGUt5t7ub1jUgeQ1geZJm4mkzgRjcJFPAG3kJ0O6VvCuVQ4/NJB3B+EbHWIj+DXSOz9pBYElAQCRP753J9JHlWksNyetsQqCPLT9elfHFODyJKgCPn5dN6aEMjQbkp8Phc4WsklwPgFu2PdEkz100+evX5fCr/AMv2NIHh/b+3+DUi3L43MH5DXr8f4671+4O3lI8ukiP2+nX615YtOqmZ2kWapKzhoA0+Zr6dRX4MYB/Hl+On8GvDGY0RqQB6zHT8Z/Z8lEXF03crmuKNon/npoPjXz+T/TX9kf8AHfrXRhlk+Yjp8/1iepOmsdPzGPl8vT+PPb/nTXeUrNrovMtJA6fx9etePMvDVuJsPCAR8tR5abTqPjvP3hL43P8Ax/XrX2l+ZA9d/wBo2IpYcPem3A30SP5l4e1tnyHKtzRlzG3ngSyi4CPFMHuvcMqqhiSiL/GcuYdlFxFZZYqQ5RSjaeFlFnJB6FCpIAmDpTa54VGLq3hADMDE+EGXOXrk96dN+p91Z8X4nbTNAMtKureIMAdASN1I1BXUGGE+KXYHu1AVTUsbfVUrmPgd20wBXIT4reUFFcDQgsYOYToSSDOgAq38g8SN62QD4iDKzHjUEo8fakZrRGk5xMwKieJ8xB7YRpKggrm8RBUaR028J6Mu4ERVe5G4mbWItxqM5VvIq2n02YD038pr2GWI3GoUFrhHILbFaerm5qsIcNeLaLaTM7HrdukW7FoeZglz5SfKupHUasYUatpOg1Og1Pwqo8wcaOJu4WyilMKL5OUkF7jqO8u3rxGhcqMsDRASorWdMqwhjKZv7XaPgNh79fILlP0VYQ180le79jst8SNT7tB4nkqPheRWMOBqcSAD698gH0BWv6Qn8KzFybwxWwwYkSMbZY+ma4lz9S1ojmbFZLbGshS1Fg4/PFdXxCIvexo4k/kuO3fsd8qhVLsTBOpEAsTrMbVWO0/m2zBtrcBZSQwB2I0I+IOhpd4jtCwdlrly9dZmAZfzb5MuYFTDKc5YAn3YAMams3cQ7Tlz3sqXHEsUdmGZhOhuaAAmQSQNzsK8iqs12MAPPh+Vlbw4VG1/WSvIygWvrc6377AdwT6tcVhp3BNTvfIwnSstcs9rTq4N22rqDsrMseuuYGPlTK5f7UcNduBAWtFiAoeMpJ6B1JAn1iprZCBqm5Y4y7sk+OyvHMLCDFKPnxcqXH+6rN9ATTQx7TSw7aLoTC3PN4tr65jLf6gY0fachxyMNuSRnM1qAD5wR8CJqLwR1qU5l4irhAuyqoJiNQoBjrpUPht6ltHZsqKolDqgOb3K2cOQEVoTs1xhfC253WU+SmB+EUjuSODB7ltW2Z0DecMwGnXr8t60dgsKqKEQBVUQANhV70Ygf1j5L6Wt4nf0/Nc5+k2ui6iKmI7ZOYHkACPW/op3lz7f6P8AaqYqH5c+3+j/AGqmK490+/t2o+5+GxdW+jD/AIbpv8T8WRFemEw5dgqiSdAPx+gGpPQA15GqDzR2u/keIQWUF7Ifz4zZZVlIyo8MBcEhwYI0AOjEVmqSgnqyWwi5A46Du171sq2rbTxlx34d5WgOF8Ky2jadjcRjLWzrbnyyncdYbQkA5QQI805dww2w1j/wbf7VpSYb2g7BUMRdtyJi5ZYmNT/kGvL0nf6V44r2hbAEqty5OwS1l/272qqJujmOTO7YdpoNTYDkOAHgsg6pje4ududTom1xTlXC3RD2E2iUHdmNt7ZWfnNcxV8PGS0zqBAuW1RmgD7aApeDH/3K3QTJi3IQJa77QNxtUw65dYL3dfLVEtkR8LlRV7t64hJhcGBrA7i+T8ycZqfUAfAVLpuiGMC2bLYbXedPDLcg+nMFIdLHbQeg/NPM9pBA1N1fS5bxdo/1btpG/CvNedbtzRVvXJ6C3iHX5uy9yv6TAUpeDe0Xil/lsPYuide6a7hyB5gO2JDH08IPmKa/Z/2qYTHEIjNbvwT3F6FuMBJJtsrNbugAEkIxZRqyrXuIYfidDGXvje5o3IlL2jvLbXA5kgDvSAWOPAeQXxxAXSs3FyaeFSylh8QhNtY6BWeZ3WIKc7ZeANiMK6oJuI4u2x5sgZWX4sjMAPvZKenNt5VVmZgqopZ2YwqqolmYnQADUmss88doZxBe2ge3Y6eAl7us5nYBignQWgV09/NMLP6L01RUyNmiAGWxJ4Du778r+JCbmsDa6UzsDB+vp6GvS2fWpziNsHUn66EfI1D27Y+VdYa5Ka+66MOkzr0P6q1xy3xizZN1sRbNyy1phdCrmZFDLdNwKCHIthDcPd/nBlzICygHIpwjD3NSdFXcknQADrJ0jc1qZW1PzHyIII+BBI+daXAqcVEU8d9CGjvH2vzXM+n1WaWoo5gPsmQ25j6u48xorW93BYcd8OM3BhiSVw9hsLnbcd2Gw9gYtlWYgRdX7VwQTSr7ae1L8rCW7am3h7bq9tGbNduuuguXWloABYBczSWLMzEgJx3uRrpcLaysCYUEkN5wfCVgCSXJAgEmBNLRbKqSAQ0Eifs6HcefoxJB3jY1QPoJIJLT3zeWvfp8d1pKGtpquLrKZ127bWt3W5qwnEBrVsmTlcq3TNlXr5SCpJ6S0SQBWk/Zh4Cj4R77qr962SGUFcqAZtGBEG5mPpEepy1gYLpbJIBdMx8iSFI+QYgn7w10rXPsxYXJhL6xlK4y+hWWIGUW9s2sQQoMLIVSRMkvNZZSXuOUkKU5q4Jiriun5i3bOQWktWRiJEnvO9F60LKjLGVSjeLMO8TQiu8H7Knt93cJVbwJOa0RbZTmJUqttFt5QsSGLQQNWmKcoFftPF125bDx4+9MRvc12YHy4Kv3uElsOqlh3iie8TMv5xpFxgfeGYs0sdJIYjTSAwfZ4BLJFtzm1mWVjMEOVcmT4iSSddCIIa/2LYAAGgAAHwr1UUmwvdOiV1iFVOXuD303unMDrnDOrADUlsqnOTGgbIBJyzGX17R8OO5LfaBTYamCQNzpGdj139atKionnLDhrLAmNU+ZLqAD8z5ivXG+qIxawSg4txMqhk9POu7ljhvdWMzGLmJys381IlU+SsSR95mGm4jO160qIoAIm2THUCWAn18J/iKnedGKrZ+ySQvUAEgbxBhYkxBAB2rD9L53Gakpf2ZHuLhzyAEDwuQT4BXOGRgyEnguTjGK8BFvKlvUNdOkwdgY0AJM5QSCCYlWAR3aqlu1byq5uu5YqAJZgeoUaQYIBGjCSsAQs7zfiWxlwBWK2LZUW1gajWDHu6LaJY7h30A8JHRyRwhXe7caSUtGJM7sFOpJkqdJ6szTsRSHPGcDcj4LSRROy66ApX9nfC7lu4LcRevnxqN7aMRCT5wJMfeA851xy3w9kQKGCwBGmaNOg0n46fOl52UcsjvmvlQWbYnoNhHy8qdC4TwyGVfWJj8VqypB1hMtt01WSNiAjC5b+BuFdLgY/wA4FB5aQG/VVSxnCMUzfZUTJLHSB5EL5H+btudYsXFcNeyQuKRTO7WSQfpdkHz33O1RvDcBiVI7zFWboPTunX9dxp+cfrNTJGZiBY+8frdR4H5Gk5m/9rv/AK2VfucIvT4XWDMjc/1ifnt16Rry2sBfBIi36SxB89u7YAdN6umKwRjW4gnQFbZG3lLnT4GuHFYO3aUy+p947fHUk+X76ZdAB/qpkVTfT8v9FW7eGuT4gug+ydPLoFP4V93rXoPPoYO86/CPlXlc4kkxmYz0EDy8gOletl1Okn4DX6mB+z4VELuSmkHkvXCJIJ/Hy6b6CPT/AIVFcQuLnAzDrtt1+X8fKuvH2xBgQNtfr9fjUHdGXX9Wp/4/HekvJtZDQL3XWmL1jf5H8f460Ld67eg8tRrvpuPptNcLYs7D46jaT1/dFe+EEhm810B20zAH5zB12Vfn4w6pThpsl9zowZixOn5wbGCuUMdfMA3BO3u+ZNI3HYqCyjKZ2LFtIkxowUExAYqdxqDqHf2lOFR/NlBX4+6fTWdesT51nsWiXOp0On18oJP8HUiDYUbb3VBiGhsFy424wOvp8QSJg+uvSPXyrm4djMr+WYhW9QHW4D6eJQ0DqtS1/DOBmKSDsxRtI+9mWGUk+oPXpUPdwrTrHn0BPwAiY8lBgdAKtGEWVS4EFa3YVA8zXksWrK20uE2hcVCfES15gzBQoLkmMqjeCdJNT9dmE5hfDq7IisYJkKGuaDZMxyj6amtl0pw72mmztaS5u1uR3+QuL/R1jvsFf1Mjmtjk3zGwuActjsCb21XnYxNzDYMLdBS7dcXMhjMggd2GA2YhWaNwInemHzn2n273DrzA5L4ssMp2Nxlygo2xBJnoR186yhxfm3EYm+mIuTbsK7IgYk53cZWJY63H2LN7qhQBHWz3sSXAtgx3mVfTUiuVPzQEAbEa+vwX0rFTMnGZ+7Tf4emnol1wLlC7fts8liJyoDBaN/EQQPoahxi79nOgwiqHGVs9u5dJHlnzZfXwxrHlWqOWeRhYAtqQ3XMCrGYk+78dwKq/P3CMYjZrfduh6RbZhG5IPij4TVnFVxNHa48uCYkzPJGo8LH+fqs58K4beuMFXCZ5+6Lqf6zMVHzBqwc38iPYRLmomM6khshO3jAAPltvTY4DzG1sHvgFj7iAT9Kq/ajztau2jbUGZBkxqB+qpZka8Xaowu05Rc+IH6X9SrNa7RMOli0bjFrndrmVRJzAAHUwup13pK9o/Nb4q5mIyosi2nkDuSerGNT6ADaoa/iJqOx93T40uMElJqixkZK48PB0+lS3CeHQcx2FQarUjwwHbWOo6VLkCz1OddR5rQvZLwKxkW+rLdubSDItnqI3DR1OvlTApNdgRAvXRtmtjTzykax5gE05a3WClhpW5G25955+a4P02jmZijxK8u2IvwadQBwsNRp47qX5c+3+j/aqYqH5c+3+j/aqYrgPT7+3aj7n4bF9HfRh/wAN03+J+LIiqHzV2WYa8SyTh3OpNqMjEyZa0fDqTJNs22J3Jq+UVlqarlp3ZonEH53Gx81uJqeOYZZBdIrmHssxaq3d91eGQgZWNpySMulu5KDQ/wCeP7ai7vZtjrYtF8GR3iygOKwUtAUnw9/mBhgYImPODTe7VcUVwzQWUHRyjFXyahspBGslZ1EifgePlv2gb2Gs9xiLGGxCqqW5u58GzJbUIouTZv2XIiZRUXXRR16JgFXLVRF85HIWB9bG3oPFYvGKZkD8sI8bn4cfXySvwPIuO0Q2LaaEy+IBG4/zSXJIzbCY0mJE92F7NMU5YPesW4Cn82j3dGzR77WvundR6SNavq+0DhspH+DsFuxzf4R8QzMSIb8izDKDlGugAAgaV8/4xSG2qDA4EhMhzHFvcMqQSSEwq+9HiAIDAkGQTN8I2b39FSEzH5CrfDuxm84gX75uNJULatMCo0z5e7kod80gAETrpVR515RxOCuIl7MGI7y1dVTadXtlCYKsy57Ze263LTESwOhFMbmL2luI3DKGzZEFVNnCXXcZ8u13FObBMqIJtx5g0o+Y+O3r73Lt17j3GAz3Lr95cbeFEfm7aqToqSokwomkENA09RZLjY+93HTlurT2k9qN/HWrVtvAqovfRA7/ABNv3rjAaC2GXPbtbBvGZIti2n+K4lg7EMwBYCAxA1UN0PrViwNhjkRQWdioVRqWYzIE6bAkkmAASSACar/NXDjavNaLBigTMQIBZraMcs65VBCgmCYmBMUzQ08FOBDEABqbDx1Pv/QaBOEkleFm9O7H67/OpPBp5a1D21rrwpPTT1qc9t9lJBsrryvzKMJftXsofJmDr1yspVip2DgHT4ZTEk1oNRWZuH8HV2tKzEK9xLbMACRnYKSoJE5QSZPUda00prR9FmMDpSN7MB8AX2+JXKvpNcSKUf8Aq/8Aj/RUntB5xe1nsIMneLle4QQXtsAWt22IHgaYuFD4oCSF7xLi6wVjMQo1nQAanQEnQeQBJ8gCdhT8a1IiSPp+ogr9RUzw7gaLguIYkgSMPfsWyQBD3bZRiCAJJ7xEB/nXB6BGM0PUh1S9+5GluJ0ABvw8NkdF8eZII6GKG1gbuze9xGXcnhfc2vZZoxqeJ/Uv+JP7a2n2D8SW4l9lP8qbGKiIym/YRLi5tmK3LLq0bGQSWzGsaKJM/E/UyKZvYJzZ+SYoO5JtFHt3AAWIV/GuRZADG6gBk5QHduhNU99AtxkvcfOi2TXBzJaY2boWZKEabwdGjqDlmCNQa67uIUKWkZVBYkERCgk67dDSK5w59uYhM7XTg8I+cWVVC+IxIAKlsisgCa65riJJABuESPTyTUbdb8AmYnOTNlQC0LxIBQ3J3JAIEA6xIB/YYtvDrZCKJnTf8RB6gDQE6kATJ1rN+EscOjXEXG9B3inbqDgCoI8gzD1q1cm8xugZsPde8ltGa5h78hgnhU3LbKSrKsKT7rKJ8EMSPA1w3T7sjtG6J4CovmK8B3akwHuAGInKFZ59AGVSW2HXSQZDDXwyhgfCyhgfQiQfLalhzvxnvbkAnIvhUaj+mxBggknJHkvrXt14xuqqPa1iu9Nx02Ai36KghT84nXzrm7eONZe5UHU2cTdBXZjkCIB8WcOCPuiNSKk+K2JQgjcb0uu2zCO2Awl22rlrOfD3FVSWAXLcQkbhYtBj0hxWO6TU96uknOzXPb5vZpr4tt4lW+FuAeQvHkRQpSIbMXWdSJOaSTpEuynp7zEdYuPLXB3td4mwuWfAY88RbUrp5SNuhB86TXIvOHdW7TughyUEffUIWO+h8QO0NIEjMSNB47iSvhbd6yQ5J0OxBa5aLTsV0AMaaAbEaQWwubKS7v8AO+n5/BaZ0mgt3fqr3yvwBbSATJgb6GpHialV2ny23kR/x+FKfjPOlyy2hW4IGgMEH6nw7+IT09TVcHa3cLvntlI0QFlaQeuZfDrpAOvn0i+ZMyNuUBVxp5ZH5iU7OOYy2JUqraayF1Y7KOp8zOnr5RXAL9lELAmQ7gLM5YgwADCiGmOgIiNBSkwvMpxTrbfNbLusGdYUyTJ8422Hl0qd4njmwzXmSMjhS20h1BUtGubMpCsNI7se90bFTmOYjZShRZW5Ln9eCuGK4whfMyktplEtHmNJA32zdT9avxbA377aZVXoqmD89AT/AM6XXF+0wqSFUtm3Og6Ee6NInbX91Ve/2qYhScpkawCw6+ZIzCPQn49KSZM+hUjSHVo/O3gn5wnlbu/eKFjr4m1+kAft2qQGBc/at5fJW2+URpWerPaJiLvvqq+Xjn5kgSfr+6pHDc1XTJ70DyEmIjrHQU2S0aWQC92pPom9xrhiqDqZ/D6xVXuprptHy/5/smozhfNtyIZg3pO/x0126jy2qRfiCuQRpJMj+D5x6b0iRjb3CfYXDQr1s2fTp8p0/jz+hr2xAMBRt1/WOg3/AFRvtQzkbkT+P8fjXtaUiPr+350kBO5uKXfa7hj3YO6nTY6RsRt0M6x186SyE223QAL3gLKLmYr41RRlkSR1KqJOYjYvntYQdyB94zMe6YP4e79NNRWcrXFFBKXOkhSR57z1mfKOvwNlSDchUWJHtBTXB8cVS2XU6gw1u4LhGu72GUgT7viYEiOkGpm9wa3dU91lRiN0zAHVZUqYKyw6ATIWDM1C8MxuHSST4SNRmYE9OjZgCdT4XJGmkk1w8d5hGn5MGCLGZiInce4Sxy6wGbXzinXMc53YFvgoIcGjtarSFFFFdmXyQl5z9yeLjZ2uMEWIUAmBOqjooJM9BVfvF7dy026qwnrEhss/X6itC8s8lYnEibdvwffc5UPwJ8Tfoq1WHCdiNkk9+9uFGa4bdsBVA1JN54H+oes1zzG8EpS9zo5ADr2bXsTva23mNF3zob0wxMQsjnpnOAsOsuG3bwJDrXI5g68rrOFrtFKi6Q5DaLv0Ok/TSqzju0F9lYgDy6/OnN2m+zfauIcTgbpsq5bJYvy3ewjuHRgA1sP3bEW2V/CM0r7gz7j+Q8Uv2Q39Fh+psp/Cso3Do4zYldajxBs7MzAvjH82Ow3/AIiKrWJxBO9Tq8l4o/5L6vbH63qT4N2bXWM3WCD7qkM31HgHxlvhU5jWMTEhe77IVKw9hnOVQWPkP40Hqa8Mbwy5mIytA0kAkfIxT24dy7btLltrE7ncn4nr+oV44Pmb8izZ1LWmMwsZgx+7JAIO5EjqfOp1GY3yhrzlB42uqHHG1ENK6SFnWOFjlzZbjjYkHXj3pHWeHMPst9D+6r52e8hXMQSTmtII8TKdfRQYn47D1p78vcbt4hBctNmHXoynyZdwakJrWQdHoy4Pc/MOQFr+d1x/EPpFqWsdBHB1b9rk3LfItGvj7lW+U+S7OGOZczPEZ3OsdYUAKJ+E+tWSiitDDCyFuVgsFzisrZ6uQyzuLnHifnbuCl+XPt/o/wBqpioflz7f6P8AaqYr5w6ff27Ufc/DYvrH6MP+G6b/ABPxZEUUUVjlvVRu2k/9Vf8Aov8AqU0uuJ3iHaD1P66dvMPBbeItm1cBKt90lWB20K69YjUGYINK7m3kTE2nbVCpJyG7mDEbgFraZM8bqVUgg6RXQeibX1UJhiaS5tyfAlY/pC5kEgkkcAHaDxAVP4leOa1/Tb/7DXzj75yvqfdb9Rr94lw6+GSUteFpkXWgkgrH8lI33Olcj2LzSpW0hIIlrlwjURpksN9TFaqTDqmMXcw6d1/gs+KuE7OHvXPi38CfFK8LlkuciKXd2yoi6sx306aDUkwFAJJABNWnhXJxfLnxNpQI0tAFtB9+48D52j8KZXJ3B8Nh/cKZz7zF1e63XWCWidcqgKDsBVDV4n1V2xRue/kGu9dPhdO522vew5qF5N5H/JrZd4a+ywzD3ba793bJGon3n3cgbKAKXvPHJP5Rfc4Z1bEiO+w7yjHSbbWbjgWnmyFLJnBUqxkzA0Le4kmUgKXkES0oi9JgjvSy7gZVUx7w0NU3CWkS4Cgz3CQ7N96NBEdIgaTA6mKldHcAxWd0tXUtMfZOUOsC53BuU9oN53ynaxOqr5cVp4ntaCHXIuRwF9SDte22/gs9/wDoPjQSDhMT4d4s3GH6JUEN+gWr0wXA74OX8nxGb7vcXp+mSa1lZ4nA/OI6H+hcZf6wSPrHzrtwuKVhKsGHWDMeh8j6GoVbimI0lxU0xb365fJ1iD5Faalp6SpH1Ewd3aX91wR7kl+zbs3vM9u7iF7tEuW7uRtHPd5jbXKPdGchmzdECwc7FGPbq0Cqxa6VrPo9r5Kt1S5/Dq7AbD+sXK/pcpmw+yBv/W/8S9krj575nZcG+GjwF8MfVyWxt5wIGpVrGFEebfCexK9DYUwWElTKejbZvjBYfOdwCNritD7XEGXtYg/l8CVzvo5iow6q64i4yuFvUf8AyAHhdKWxytcW1euOCps/koZfXEm7l12hRaKkDZioMQRUTwy8VM76ajzn+J+UU6+IcOzrdSSFuqgYSYzW2Z7ZI2MMxbXr8TVbu8h6XMvvQcsn3nF1jm8gHtqoHQFifM1nZcLlY4tAuNfQ/Eiy6TRdKKaaMPecp7IPiQNfAG48BfiFauzntLe1YexcLNZNq6tp1BL2iLQt2kVQUlAbcAAqc1wsWEE1fOeOV8Hes4C73uSyimyGUqVdcpKqbnuKyujeIlUP5wZlJQ0mMBygymFfxlQ/dsBJUe9qCd5EEDRgy/zqvnZ1zZbtJcwmNR2skuYbMzWWye6q6lVMShtZcr3GaYOZa57HRPyu3Wkp5mTsEseov7/fv8FJYflbCrp32HjSCDaYn3p3vwNp94k67RreuzTl7Co1+9bcuoXuyTqiqQHuQ2VQ0wJgEKNAzSTVTw/+Cxe7sJdKh8ne94+QkvlMa6oCZzmAUUkEnLmk+cebLItDD4SAhCszJI0MtkEwxZoUkmSQSD5UgOPFTaid0wAI9Gj4AL24xzNFlcMkhVRUdmJmCEOQHSQAWtEnRoJG4qvYe5Pw6fx61ZuReTyxJuAQNh0WQV16Fuo6AesV54XgAHhO6wra65hOaR6nT0CiKXEwybKFUzsgsHcfnVSXLWBDIzSyukMIiChgRBHx9IaCDVX4jaFm4yuoOHvQtxdQqa+F11lcpiSCCAARqi1eOC4fIH9cqL+Dt9IUfOuXmbhodDI6UzVYZHW00kEw0OneCNiO9psQeYUSStLJWOjPAFIntk5HW2QlpSFdSUPvOCWt2mhj+cZsxsy7szkA6n3Tw9lnFDhcMUuSbbXyEncI6oGJ2hbe/lltMNcpNNDhXC+/s92znPhrpyzqchGZFOs5QwEf/CA2pac+cLzM9m34cuGxN1gBAj8nu2ra7xJe+rQBvb2EiOZ0FRM2Z1DO6743Wcbb2PZd95uvmug0kzZoRLbWyXfH1dLzyXcKSFRWZRckqULPBOitqBBLLqdwasqYy7e7u2AGJAFqTJkZhIvSVDD3ZZZkbTFag7IeDq+GV3XVoLGJJlQdtW31gTEjSoDinZ9hcPfN8XbneTbILlZi3GUFntZGCx7sK2URrAnX07miMF3zzTM4e52WM6pP8u8ZxNi+qXLMvbIbu9SfMldTHyZl6SJrQfOvKJYMGLZT16idfLbWPl8aX/KXJX5ZxKw+a46pdVzdkqAqkM4WAo8YGXQCc23Q6b50CqhMAnpt8twae6mORhcNrpz2iWGRsb7FxCylxTs9Kq2Vi0fj1P8AE0qe0jly9Yt53BS2SAG6s33QN5gTHlrtWqsZgyZI0np+uP10s+0zgN1lzKpbKIjeB5r+Mx+uKQzK2xaFMmjLwWlZmt2G8LZ3IYkCCSBlic+ukzpHlWkeX+z3hlzAWrzvftYm7aVra2rj3XZyBtZuFlKsREnu1hh4hAqh8KsqGAuqrf0kUkfUT+NMPl7iFlD4CoMAeEAbSNyCY6aRTzsRFiMqq4sHOfMXm3IfP5FUn/0VxFlt9oDKSY23B8QB6a3CNdIqz8Ev3V1yM3pvr8Yy/Cr/AGcMLoJ67gzm8idN5PWjCcHhgY3J9PhHx386rpBmKuGMA0X5gASikqVYiSDBiZ6jQ/I12Wbkfh9Tp/HzroxtrXT5x12IPT/kT51zu0HT1+EgiBHqJpsixTzNQqr2p2syADdT4jIGyMQP2xHQDpSKuctm6VKrE65oJzEwdTqAACBoAOpBJJp788Mch+IHoRu2vQlZ13ketUzE8U/JVD3QSTcEhAoMMSzNAA2AMQAD4Qd9XmTOb9ncqFLTNkf2tlQudeTXt2O9CnKsAwAADvrGknXTU7SSdTS+HYYu1sa+PMr9ZIDMvkRMQTJ861BxfiWHv4YogVhcBBZlKvJEaQzCQTtABgaDQVnEXDaBX/KWrpA8/wA5bcb+QIKx5sp6QbCjmLrg6quxOmEYDgLA/ktM0y+yrs+F4C/fH5r7Cf5yPtN/M9PtfDeq9n/LpxN9UPuL47p8kG4nzY+EfEnpT85t4wMPYGQDM0W7KgdYgGPJRr5e6OtdHxrETEOqjPaO/cP5r556GdHm1bjVTi7GmzQdnHme4ep8F64rFye7TwgCDAgKoA+AAA8p6eYqlre/L7vc2pGCtNN19QcQwO2YfZJGnmAW6LHLxlrl1lwFlizkA4290E6smYfHxRuSF6sAx+XuDpYtratjwjc9WJ3Y+p/cBtWQtbU7rrl+AVT7R2i7gVA8JxVxDGyj/B+LKfLMco9WrPnOXC8l1wBpJP8AHzrSnP2GDJHVblu6h/nIVKxHqpBG0SDIaClOebSvcLAhZ+y2hB/pR3cepYH0FV1TGS+4V5hVSyMEONktjYr6FmpzjOEW2mcspH8x7bn+qjEj6VS+I81RpbtknzfQf1RJP1WkRQPcdArabEIWDVw8tVI8RdUUs5hR1/UANyT0A1NK/mN2vtmIhR7i+Q8z0LHr0GgExJmsW1y62a4cx6DZV/orsPjuepNegwXpVzTUmTU7rMV+JGbst0HxVU4J3ll89pip6x1HkRsR8abvK3NouALdAR/P7LfuPoaqlnhvpUlhMBV3RzyQHsnTlwWPxnB6XEW/WizuDhuP1HcUwqKhOF32UAHUeu4qZt3JrSwVTJRpvyXJMVwKpoDd4u3g4befI+PldTPLn2/0f7VTFQ/Ln2/0f7VTFfOnT7+3aj7n4bF9Q/Rh/wAN03+J+LIiiivysct6pHBX0tr3jMMxkKPuebH1I2OwEmZIiD4pjlcEGGU7jcH6fUEbVcMZw9BYW26glvG06EEjSGEMpVYEqQQc3maW/FuBOmZ7TI4EkretqSANdHt90W/S129411ToT07wmhpvZJWOY4E3cBmDzz5g8LWIsNDwXJ+kmGVVXVOma4ObsAdCAOA0tbje/FRQ5Y7y4e7MlLdy6FOhYoUUKDtmlwQdNQBpMijDCNJ0O5EETB6ggiQQdxoadXZnwi87Peuolq2Lb2kCqQ7sz2nZhmLZUQW8hkHMzQIyNMnx3lWy5lgZ+8GAb6xr8DpXWKWrgrGCaI3adtCPjZZJ+eAlj9x3gpN4DCt/NHyYf2jU1hkbzH9U/rz/ALKsmM5OUe5cf9MKw+qhCPjB+FQmIwN1DBRj6qCw+qzHzg+lWIbYJkvBX26iNZaPPb+qAF+cT61Z+RcOospc+1cGdm6kkkjX+aPAB0gndmmnHEdDofI6H6GrnyFwy8cLaAUMB3kZXX3e8crMkENlgEdDXjnNFrpp40UniLoqucYw8HOphh1G/wDxHodPOrBe4XcHvAL8WH9mai8UtsaNfs/0VfO39Q5CfkTXlRJAYyJCMvG+1u++ibic5rwWXvwtv6L74Nj84194RmA/Aj0P4EEaxJhrXSpDhXDSDntklAQrs8rIMkZLcTuDqx06E6gx9rpWM6K0dLTVtW2ke1zD1RGU3y/1l2320O2psLA6qP8ASLWy1VLROmBDh1wJIte3Va/r39y9krosLJAGpOwGpPwA1NSfAeBZgHuEqhEoqxnuD7wJBW2h6OwYtEqhUhqtODshRlRQgOhCzLejOSbj/BmKg7KtaGqxWOIlrdT6LOYV0VqKlokkORp8yR4cPM+RVSfCFFa5ci1aQS9y4YUawFCgNddyfCqIjEtCmCaqGP54OosJlH+duhWuH1Wz4rNufJzfPUMvTj7V+Zu/v90hmzYYqI2e6JW5c8iF1todRAZgfzhqtYWqSbEJpdzYd3zdbmg6PUdLqG5jzdr6bel+9Xns2cs952Je4MhliWZgReV/ETJ8QtLrt4R0q/cT5ft3R4hDRC3F94aEKZ+0BIOU76ClFwPHNacOsTrodmB95T1gxm+IBHu02uA80WLiKC4tOo2unKuxEd7GRlIjchpAOUECqWoY7NmC19LKzJkK4rPJRB/ldNIlNfeMzrHuQfVtNBrVu5Z5eS1qJZzpmaMw3gLAGWc0GN9OkAfuHxKkaMmvXMpA8zIMGOkGDprBmpngPFsNbILXUME5VVu9dnZiSStvMw1JAkak9AASyAXaKQ4tYMyvHCcJkQL13PxO/wC74AVF4yxb71mCjUBbhmAxB3kbMglcw82B2r6xPFGbQAoOuoLfLKSq/EFjrplOtc9ryG34fAen/LXWpjBlVa45t0W8DPun1yto23Q7E/Q6da8cYPCQdPQ6fhUglcvMWPhDmhhHXcfA70+2YjdRH0TSbt0S24Qcl3FXSYRbYUx1YkkR6gCP06p9vhjZ2uRLMZOs/m/uGTrlAEn+jpotXDgl0W1v3Lrqlu/lFlXIBMBwWjqGzLEaws7EE/ONwM28wYIFOZiyyxAjMoOZd9B1Go32PJKl76nF6mSIjKHNGYccrGgi/wDCbg9+i2+DNEVLlduVO8i8JFvDW0P3BPxIHX0Gk+gqt8U7N7d27nJdz5O3gXygLE9d46bUy7GHBHpXhecKdNP4/wCFbcQCwB2Xrah2clu6++UOCW7IVUAEbn1iPp/x9ag+0vEzmUHSBr6zVp4Q4aRvA1/HeqTziviOmp3jz/5frp6UfV2CTRa1Jc7cBVy2xgEif4/brXlj7gOyjTpptHz2+O1R9+9f1CrIAJ+PnGkn+PnA2OZIfK0jWCANZE/Dr+2oeewWiZCTryUrjOVbVwyyD1MefnofPca9a98HydYTXJP7vrPnM7a1LcLxqGCDv0mY+RgjpvruKlRcX8ZiNvmAf+VNloOoTRBBUbZ4Sg1AgjbzHXf18ojQUYi7A9fMfs/g/rqSxbgiRp+v69P1RrVc4jcPTz/Vv++kv7CS3tFc1zE67ec/rn4Vz2mPwAkz5V6XfrXhjsQEHxkD6H6iSB6TvUe+qkkC2ipfHcRlxKoxnVWE7SSqqTqRImI0jTyk3huAWWW211FYM4zA7ZD4XX6eIeRjy1Vt/GB8UuumZgWjQiNPUAzqIWCB5sCwMZhLt0WURgqKtw3T9tpIFsKPsgLmkCN1G2gUxwDrptoJNr25pU8KtWxdxFxSRhrBfLJmQhgAa+IsYHrM/BQ3LxZ+pa4VzD+dmY6egJB+VOvtodrVoWlCpaVoCroGf7MgaaAmPX4ClS2ECqtwe7bhWPmxGfKTr1efXQedWNKALnmq3GnXys4AX/mt6cjLbwuDzZlN68VdxmGZV/yaEbiB4iOhZqOc+O/ns6Q5spbt4cSGBvXRmNw9Mtsak7ZktgxNUuis7P05mlkMjohc95/RQ6HoXBSQMgjebNFthr378dynDyDgrGGtZTetNcfxXX7xZZzruWkgTHrqdyamMVzFYXVr1kfG7bX9bUhLjQJpadoSO+INrvEGUgKhibjaiQCZIQyANNidS01JpOlMlQ6xjAA7z+ircZw+HD2tGcucdha2nM/ktWcV5hwzDW/Yg7E3k32IBzaidIG5G4gGqPxVMK+92z8e9tTPkPECT6AH4msr8S4oQx8MsIQBpBDtmgmT4FA0yrqTMV9ccwTF7FpJ8Vm5qNDLK1ydNiypHzJEQIs24m/S7QN+PLVUPtR5LRGOt4GCO/st5/nbQ/2lxBSz5u4BZXx27tkodou2yw8wQG09NTpWZOO424t1ranKFIEACCY8RYRDb7HQCKluGsqXBEAMFzADTMVzbfzTmHnAirYVD4wHW3F7JT5LjUJwjArocywesj/lX7hUQ7MpjeGUx8YNJHGc0XBbyqxUq5Ej7oMag6HQKNfL1q08EutdtNdRw920uZgAA+QSWDFQFcAagwpG+sTTr8Ulibmcwb23P6KM9p3KZuVPvL9RUjw3DAmAQxiYEExtMDWPwpSca42wCOihlcZiJgg+hg6HyOm3WaOG81nOjpmt3rZlTA1H2l0lWVuqn0MaCkf05PluIx7/AIpLYwSM23G26eacNb7rf1TXVZwDeR+h/wCH4VJ8m8fTE2Uur10dfuuAMy/jIPVSp61M1nHfSFUROLXQAEfxH9FsB0KpqiO4lJa4fuggg+ajOAYZhnkH7PQ/zvT+P1y3dHyP0P7q6eFbn5ftqXs1NZ0cb0jb/SkshY6TdoFwMn1e9xuGX24rMy9KP9l5DhFPCHMi2JJBOf6w6WOxeQNdlAdw33W/qn91d/COEO7LKnJmXOTC+GRm0YgnSdpqesGu6zTb/o5jG05/7R/9k6PpJnkbYQtH3j+ihudb+WXcqqkgZmZVWTMCSQJOsDrFRGA4YzuqlWAJliVMCDA3EaQXjzVPOl77beNIwWFtru+LzgefdWboH+tdX5xT6w7AgFfdYBl/ouA6/wCqwqFS/RxSwyAdc4gcwNdr6jx/NMTdIZTAJAwa6b7b2+C4eIkgBVUwAAAATAGgqCxCOfst/VP7quSTXLxLi1q3pcu2rZ8rlxEP0ZhXWaesELQ1rBYbLKOeXm5VOaw/3W/qn91eF3Dt91v6p/dV6wuIVxKMrjzRlcfVSa/HFS24qf3R70w4kJV8cvquVbpVc5hFuQMxkDwq+rGSBp1I86XnaV2bi4Dct4dheWc2W22ZgPLTNnXoOokbgCoH2keMd7xzC2QTGHbBW4HS5curfcjrJW5bH6IrV3F2l2P3ob+sA37abdiXWOyOYNr+tlKma6nhbMONvW5/JIHs+7OHe3aa4gtk27RbNalgSik5g7WzmBOoBJB3g6UxsFyXbtjVmf0ACj5gSfo1Wp68Hrm9Z0CNVKXmpcASTYMGnne6mN6XuY23VA/eP6LivWQtkqojxpoB5BvmfifSqdwPh2d1V5VN7h2OQasFOnjb3FjZmBOgJF3umoArIikl56JAsh+s64ak6ZcnK173z+imUeHR9LC0z9gQOvYa589rg3tYDqx43Ks+K4h3hz5VTwqMimQgUEBZgAgQQGAAIAgRFV3mPmBBh8Q1q9bNxbVzIVuISHKNlKwdXEFlG5K7Glf2o85sveYe0csTauXPtAzluIgEAFdUa4ZaS6pkAzFV3uIRCqIVdh0E7/MwJO5gbxAm0VfLOzM5gHmtDUYbHE62f0TJ5a5bVrWJl7S3Et4Y2Fa4ihjcC4h1BZgMy2x3W8B3YGIJHFiMObbFWygjyZWUjzV0JQj4H4xVMwvFD1g/EAD6CAf0ga7cG0zA+Q6T/wAoqwEruIUT2dvByuNhh5iPjUng3HmD8xP7v1VVUeUHodfwH4GR8xUvwzEBY/E9T8B5epI60dabbL3qGg7q1YWKufIeUXlL9NUEMZuSAg8CudJLDT3gtUrhmJLR4VPkGZZPwBhvpU3w+8nkUPxlfnJkfEDSmjUO5J0UzeacuHx1s/5RD6Blj5iST8zHpXdbxiffX+sP30qLiZzn2f7fq33x6t9rzaT9rWUwbg7ifXZh8xof0gfSKOuQKfmU0LFwHUEEehmqX2k4s5ci+85CqP5zGB+Jrr5ZxWUkHVWG/wANmjzWdR5EnWJqI42f+soW922HumNZyoSI+cH5VX4niLqWmkmAvla5w8QCQPelspw42uqF2jcgtiMcly5dy2LGHt2k3GXVmuFR4QHuAi2XV7bIoBVg0FezmZlsWEVnE5QtsBwJP2fApCsYEADMNgZjOVVxvtPuYjEDwspMaW1BZJUXFQOxJHhI/k+7JgyWGtdmF4uLuoVS4JzPLOwUlIAZj7xYmGMQJjMMtYykhmho44JNw0Xtz3Prfx3Wup42hwWpeG4r80D6fsBHrVM5r5kW1uden46b76efn8ameELGGtxGltNiCNFC6HYj16j40nsVN7iluyfdXxQdpAzEkE+W3mRHWK0zpHFrQOKapY2h7i7YXTOXnFMHhkNxouvNxwdyGJCf6qqvxB9apPF+1a24LAHNJkmAsfKf49aYnPj2Ww9xHVXXIRDARPTfQEkCCYnTYa1krivMKYZnTucgDFTDu0lTBEuWgAyNMuwkeXsjjnDAeGyepZIADI9upJufnh3JmYXtctgnOLgzaSbV4L8mKBfnNfvD+6xVwkCR7xO3oROxgdPT40nn56ZgWXKFBXw776QC0n5wN13q0cr84lCYYAkjQRIBAIYZtvKddyTtSCLfaurKKtjIOXj5fqr3xjA3sKc1ps9ob2zuB6H06+ldGA5xVxGx8tvpP8aVCY/n1GUgkKQcpzEAZh72YEzGmsag9R1qv+EFzyF1IUkLB94AyPQk7ED4neoZNj2dke0AnVOfCcSkbzI+On8a19XnEfAfEx/w/ietT5aaYhpBAI3/AFeRj9dT5vHffyJMCBr5HoP46JdJmSXW3C95ESdB59I11naBr8qrPODDITrlGzaSCRE9Op+oX0FT1y7MkEeHoRpEGR5SYJ/RO1UvF8Wzq6H823iLKeuviUDofDEwB41OqmaS7klNPFL0Yo5lllyhpBB1E5RPhgyIBga6aZqdPCOJIFVpWSstrJmDCjz30Hr16Z64lNu48aknQrMEGc2siZ10K/IDagc2cdvF3i7cCksuUOwGXyKAxBB6jXxTOtT6Wl6w6FVtRiDaYEubfuTG7aOckvYlLVs51tNmusIKlx9kHYx16TpuDC4x+NZ0yz4Q2YrPU6z66/qWofh92Ou+h+AM/jA1/wCNdNt4OnQ7fx+v4VbtgEeg4KhqK59SS93u5Dkt3183HABJ0A1NfVRfNN4LaYkwoK5j5CQCflXGIWZ3hvMre1k5ggfKBfKCfcLqpcwc2tcuCzYR2IYTCtAgj3jGhDfZG0SSfdql8yXGQsWcrqZysQC3VoSGZumrH4SYpi8J4zaKi3atXCAI0yW7YHQtll5O/wCc1OpEdFfz8We9ktqABu0eEHoFncLBMtPUwYAGtoWNa7I1tgPf4rkFVWy1cvWSm5+HcOQVfxoY5WXUtqS0/mbLeBniM5e6CVDkeI+7m8NNzF4ZO/sXIIi2ZMafmluWri6bEC6lwfzVYDU1X+VOTGLLZTW7fDXCzzIZcttLlzNqBaLG6q7gIsakAWTtOsLgLVqwJdptOisvjZLQCiAdu9uOlqCQPzgGulSqh/WlrWd48b/pb5KZyki4XJyn2W2Lhz3Eksxn5SsyPgK5Oaewu0Je1dKgHQNqJ1gA79SPhTq4RwY4exaLgIbdpO9LmVF1lzvbUiMxUtBbQCBpJy1FcW4jntWyFE+NjJymSYBKSW2Hz6QDSmzSNd9oqdYgLNnE+xkAGbpn0FRXInKVzDY/DkNmQ3AlwbTbcFWBGxEE6U7eaWga9egBP100pacT4hkYMCQymQY26ddNianNrJnNLCbggjXvXm4VC57w3c6CciXbqLH3RcYCJ8gI6jcda8uFLLW9cwYSrjQEbEEfZdSYInyOxFSHaDiw9jL9pWZvjL5ifowqo8pYszlnQGR/SIIFWcLC+nudxdRwLtutJezdiB3F9PtLeDH4Mi2wfmbRpq0lPZ/cLfugERctAqDuWXISB5wHJI367GnXXN8cZlq3HnY+n6rpvR2XPQs7rj1P5Ls4V1+X7alrdRfBxq3y/bUvbFdm6En/AHND9/8AEeuF9Ox/v2f/AA/w2LosVIYeuKwtSGHWtDIVSUwWbPbfxRz4BR9i3irkf0nw6j/7D+Nd/BvaZwtnAYZO5vXsTbtC01uBbtjuvzdtmvEsYe2FaERyCGBjSeT2qsE17HJbAnJgUj+ldvYkD/ZTWXIqvmBFnDv/AEW0oI2SxZHDa3rqmvzz288Qxcr3pw9s/wCTw02tOga8GN9vI+NVP3RtS6t4vUkgSSSxIkk9STuSfM661HCvtKSx6mupowLAKx8LxmVs6+Bxs6Eo4+DoVcfI02+SO2zHWIDsMVbH2L5JeI2XEAG5O2t0XuulJHBg1ceWcGX029fU/wAa1IaLqjq25DYa9y6eW+JviuOWr9xcpu4vvysyFVJuooaAGCJbVM0CcswNq22b2Zbbfet2/wAFy/2azp2GcgC/jCHE5MLfuWz9y6Wt20+IKu6wdIJ8ga0Nw7h5tWbKNuqMNfIXbuX/AFYpmNwbPlvwKViBdLRB9rWIHuuPzXy5rwuGum4K5rgq0asfIue4ahsOgJ1MDcnyA1J9TGw3JgDUipm7UdwoL9rUHp5kFCPTqSJ2ZUI1ArnX0gEB9Pf+P/Iuj/R24iKptv8AV/51nDtZHdYm+GjNnDkAzrcUXoBgTAuKNPWq3hbGkt0kt6neJpo9tnArdu++Iutmv3QFsWRBARM1gYpwZgOiAWs3VSyhzL4ZfJZzZLY3cmfgd/w6/Dzp6hdaFg42F/dqtHUHM5zuHBTnZxyc2JOd5VJ+Ub7aT00kDUEnYF8cA5Rw6AAWxPnJnyJkQAT5gD5VE8m2VVFUbAf8vqSW+dXLhxr2WYvPcoYbkCisX2fYdgYTJIYkrm2G5mSgIzAhTvrowBFRz9kJ7xBbdsrJnzuuiMCAbbZAwzQcyksA0MBOUksrkvBEm6SNDckDefBbQnYaEKNDtGXUCrZe4WrLlJMEg6EqfDqNVIO8HyMREGlsLgbApoz81nvjfJ13CiWi7ZY+8u6k9dtJj+idNdNOS0Z2Mn6fWdJ+ZrSHFsCrKZAZY1VhIj47+vnVI4xyZZceBRbbWGAA+AIUCVOskyw0g7gvtlse0vWuuNFS+CXtB5wQfxA/EA1OWk2Pn+H8fsNQtrCNbuFHEMGg+usSD1B3BqZwjbV6RYqQ03apbAPGv8bQfw0rx4lIZbiie7MkeaMCGXyOkxPULX1ZFTXBbEsAeuh+B0H4xTdTTsnidHILtcCCOYIsUnMQdFm7tL5GbDG7i8OO9wzkXAwXMbByraKsgh8mRchP+T308QFA5D4rBZYOVhAk5RmkDf7JlEUxuI0011pzBwO5ZJuWGKncjdG/pLsfKdCOhFKDnLg9lbdzG2EFsPcWzicNl0/KGKEPZ0yiZFzwgEETuXFY98c9FaKc5mu7LZON+DXjmQLBw0cdw0nW8pK1rngO0/NODs7x4awqdVzJHllLafQACOgpP8188LhcTiLkKGmRoQzICV8LiDqUY6EgTEyzATnZlzLFszoScykkQwg5svumdcs6xDaiQtUj2ieCG6wuWV9+QQI3VDMGIAyjNBPukGBuZrXCRjWuNrWvZWbozHI9wG91G8xdqBu2mGYgssmIEgZkg6iDAKxlgysBdwlOb+MXLz6+Jm3Y9RqPgNNPIRVl7OuR2xV0p3jBVVwjjTOAxga6w0t5xEaaV181djFy02Zbuu/vjMJ0336wD11q2p6eJjs19VF6qeoblYB4XAS+wtu4uYZSQR5SNxp5R6f8Kl+C4g2whu/m01Ckg/nDJYAmIIAJ3MQB6V73eRr3W45ESZeflvv6VLcrdmdttXJYzoo1gaHYR676bedSJDHY5j7kqPCKwO0aB3k3A9yrXH+P2y35su20iAVJGgkEwwAAGsz13M+GE41cUy2YjUgMPEWOu/QZjmPpIrRHJXZ3h7Xj7tSRsCAVB0M5dtP4mAapnbDy8uY3VWNBMe7MjL4h1PXygedRnTxNswC470qagewl2e5HIWHxuVZ+yrjP/Vy7yNZ03WfEGHQLrmGnzM1LcY5oOi24IYyQdCJABEMSYcaxqRJ0pf8AIfHsli5IlQB4VAgdGzA/e1mCD8OsYL4ZhlMAEMoJzaAkiNDqNY+nSqx7O0RwUpknZHgmanED3d8FiC3dOjQehgjwjY95oTM5yCdjVbxIZw7b5hKmSQAAQHGpIjbxCAANetQGN4kzlj7uUCC5+5ltklQc2oga6+WbKQJXhWN7sSGIDKVMeIgNlYg5gSMzKCsjVRprv4WEJYlFrKs8yXGaDOwht9cums6kkTJj56lQneJOSxPqf3U5uZIuXMqZsqKTqQSwVcxErroJaTqQZ0kmlRcw4ZjlM6kA+Y89ddtfOrnDrNBus/irS4gBcXDsMW+ZAn4mKnuFYAMRmOXxFCSNJXLmGnlmBI8iN5r3wVojIq25OYHLrL6gBfD4/SF11MEdJbiyu7AxlZ1UOipo7KSFNtEVbYCo2kANmN0sWLyZMkubuVfHFlWv6iec7SthsQHOVe5uZmicoynUDqQYIHUwOtSwFV/mhLlzwZfArSVEnvXGqm60ZVtWzrkBJZhO8Acgooi+QG9gLH571t8ZrRT07hbMXAgDy1PgOPlzS8wHETh7i27i+G37wmI2ZyFGjOdsxmBr8alzdx8XO9ItGLdvvAufKArXEUFj4gTlJaTMws7kG0c937f567dtsTCu0al85kHJmRQIMwTtuBtS75h4j4CgDRfyM9y5lDPbWHS1bRBlW0GUMTmYsV6Cc23ooQ5weRrpf4njy+d1ydo1Ws+wPlhHsYTHMJV+/UWhPhQ37iqWckG4fCGIIEn6GPfhCcR4tdJZUtYe9ZyWTE3BaVnDF8xLDvSWW0sIkhyS+TJc+xbAOvBeHlQCRhkumSAJujvdSSNJaSZ09KoPb7wzFYRBif8AqxZwe9VXNm5bJywEY5u+zFYgBCSJgiYbcx7XFrW6XBvoN9Nzz19VYNaL2TB5hZEdHcSiW2xDhpuJmFuQDrsbjJAGhNVfgtruMGRmD3R+TNcfSbj3rHfGST7qKykTS27KcTiuJO+HutiLNi0lq7fZlHeXLbFmt5GylMoyyCQQYgKZJWqc+8zYgX8QisXDOugED8yi2kUDUQLaKp+Br1jCHZTv/px8LL2TkrVxm1cu+Jibdr7PV7nqo2j+c3yBqic42VAICn4nX61x4HnDiOLYoFuDIhYwiAC2hVTlYkK3vbKSd/KuLiPE3IIzlo0YMIIPkQR+O1SDTviIukNCp3HsOT+cTxAfyi9U6SR90/e20+kLc4aUQXNgzACNj1n06fWpbjlswXUlWAMEEg+okdD5VC8L5jItm04lCZBjVT5j0HkP3ReU+cx9nXXUdyQ6MjZXjsz4/wB3eRmIUghkZtFW4BkIbqEuL+bJG2YGDkitN8s8ftYlM9pww2ZZGZG6o67qw9dxqJBBrGa8RcEGVZSRqqg6fAneOlNjgePTCJ+UYTErcuG3aa5hntwLgz92baOirmYEwoAkGFgEMDQY5hAqQHN0dsN7eB007uSvMCxN1I4tOrDqeY7xz7xx8lpngg1b9H9tTVpKheVmJBJBUlUJU7qSCSDGkjb5VYrC1tehZtg8I/v/AIj1z3pu3Njk5/ufhsXvhrVSFm3XxhF0rtSr2R+qraeEAXSg7TuEA44XDqTZw6D0h7+//iN9TWMebuGdziL9r/N3bij4BjH4RWzueMdOLadg+QfoIJ+YbMayj2w4f/8A6OLA++rf1rdtv20xe+i0tEMm/L9FSorswdia5bW+tT+Dw8H4ftpMYupNZPkFl38E4UWYAbkgD40zeZ+WvyRLc6MAC39I6wR6CBUf2I4AXcfaVoFu0Gv32PupatLnLMfU5VjzYedcna/zwMVfu900pmMN0+X8eVOh1n5Ry1VLkL4i925IA7huStHeyVfW8l+8IzAW7bR0ILNAP0PyHlTa5uw0ZCP5w/Uf31n32A70f4QtHX/s1z5nv0P/ANorSPNC+AejD9RqqJyVt/nUK2nbnw8t7vgVTbluuS6tSlyo+/V/GVhqhgC4boqI43x57GqYP8qVlmxcDGAx1e3ftojMvdMFZdZylQr5SwSZvVX+K8Dt3cNcNxjlWHYBlSAni8TXFZUB93PBgFt9qw3TaURzU5Ivo/4sW36BxBzJ7niznxD+Vj6rO/O+Ku3LzXb9xXv3GLXFXKwSIVFLW3e2CAMotKz92gUM2cuo4uVrn/WRPQR/q/v1qLsAlh0Uax1jpmPU6dIG5AAr75Zvf9Yn1P6wP1Gn4GWaR3LS1DtQO9aD5IzZTmMlmOUaeFIAVdhOgDmZIZ2HSr7hsMSsAkarrGsZgWHpmErPTNPSlXwjnfD4ZQjS9we8FghZMxJhZA3Akzp4RBr0wfbNlY/9XDLmJEXoYAxG9tln0iB60wI5HOu0fPnumngWsT8+S0PwGwQPDoDtH8aVZrY0E0l+TO1GzcEybZGrqcxCrrLFgDoDEtIhZY6KavXGebVsrJAmVRfFGr5DmJYBciqwZm8UDYHYuRnJo66iSRucdFO8QvTOpETtrPpAkzIjSTuKixcExrsCZBBgk+YH8R5iqFxTtPtIxW1aziWJJYW1JJknKUYySSSRllpOsyfXhXaDbclnt5SY9xpAgROqgsT5Tp0r3I462TrW5dF3c/YTVLo8wreYO6n5gEemUedRmBarLeupiEyhtHGgA+0CfeJEjKQpK+HQnVtYqGEuxodD1+PUGnmC4Sw7LurDYqxco6uPISf3VWMKZqxcryHHwI/D9+vypTh2Sgm5UvzAs2z8KUXE3RbNgMBDY0nUZpcKwXQKTPURHu+sF04+zKEdevz0pS8z8vG7bu2AwS53gv4Zm90XUEQfl+smPDWZ6RtPsrZeDHtc7uGoJ8G5sx7gSplHIBKLpb824wWbl6HUSQVBGRgrHKYVocKXcsxZiAEDeYHHjMab+ECnMrkuhDDxAhixGvRDnbxQVVFHiLCoXtAs3s91LyMrlLjItzYyLbCNCrBDKFkBAyrsozCF4c9xwnu5iAI0RsrEORCsJzsTaYyxyosnYrEhDXszNsb8uI3Flqg8PtdNXsX4VaURoTJOxkAZfJAiqCY30OkCK6u0bB2nJU6QGYn5qFj4sQQARoXkiAp8+QOIZ0NySXYZYgAgK7oQOi5igZiV11J94RZMbg7VxQ0SwgEnNDERLOZWCujCCusmNQad69wblB1XuWz8wuPBZz5iwSW2vAtcYLbuXFEgfyZXfTrnCafaS4QSBFdHL/M6rHdCEKqZaCwnNmkwBppERoDtIqY5g5Ym42ZWECWOgDbDfWJgLsJJUAEkgwq8HS0oMsRBUCIOm0CQSQTGozAsN4Ip01F2ZSdUr6++ryR4q/8ABuOAjxEQYE6xoCfP9XpoarfafjwVKZioYeYgxDHr5kRJ0PSoLvg2YQSApJmTrplKnePdj3dydesNxTF95K7qviLNHhEhAogmDJiNRLRpJNJjYTZE0wy5VVr3EjalQSASdtJUkyASCY+IMa6V8XcUcpmB0IEevpEAnzJE9ImvPipzMJaYgddBqYggEbknKNyx1LVG3rQmOk7DN6mDOsjbr8fO3bGCAqN8pBsFY+HcddS5J0hSJBOi6KANFJ1zZuratLEGvi7xa44y25E5YWRtMeWWAfFssSdhXlw3hxYQggQAWYneTB08QHQSJ971ls8h8mlGd2ULEBJOoGVekQxVQTIIGgmSZpqQsZrZPxNe+wvooTh/Bu7wVxm1JtNmJ8JVj7qqxlZWRJ6GQ0wAEpgbOh/mmSZ03AkfXetX8b4CMSq4YQnfMlvMF90O0XGA08QUsYkSd9yaz1zrydd4fi7mEv5ScqsrKfC6N7rLMMJgggxlZTvoS1RV0RkdAXdsjMBxLdr+RSMTjyuaO5T3IvFbNt7NwhjeV2BWB3bIVynxZswcEk5csFWJmUCsy+JYjDi2n+C7l5LxUm5labdtFk3O8NyTccAeFl8I7vP4ASKR9u0I69CJ01H7eoI/Gal+CYm4pyIGm6UkEDxTookLmIYGIA1nrOsh2xIUIDXVautvBkbjavwUUVyS52XQbC90nu0T88L+WVCCGYiC57wDNHQDUD0BOkgUmONXmBIYiFQtZkiQACSkbwsSPIafB+domAGZxOXMFJ8OYbTGhzCSSdA069F1XXLPALdzHYa02V7j38MtzQwqNdRTm00LKSoU9W1ECuh4XO3Jfhv4aD58Fx2eLqqh0Z4OI9VvDlG0MJhMNhyNLOGw9mP/AIdtbf8AZqhdpXCMNadcTdstjcTeYLw/h5IKtdIzDNMggEF2uXJt2bQJy/my1X7jFwG4zN7iAs3y6VX+zrDhi/E8UVFy8pGGVyFGHwehSM3utiMovOfCQvcoQDbYtJgf1sln2yt4nh/M7AeJ4BONOpKmsLxG13NwkpbdZt4iVFtluqPEIAHhPvIdQyFGBZWDHIHPWKtLeuOLqyCWEjSeqz5RpMTrtWjO1S/w3HWyr3LNy4BC3LOI7u6up8IeywcoDPgYlSekxWdsfyDhbDh1zPrI7x2uZSNZ1YieoJpqcR5xrsNLD46r0jmF3YAfm+8sAQ48SkQZ6idwQdD0/XVdPLklrlwBdICjf51KcP4hkcge6/T+cOvzGh+AqG5k4oxlfOmg4pIFkvOb2AVwPWlzatzV65ySFaetUW1cIMjetLho+rNk47ZdfCbYLhSYkgSOh/j9XzrRHZlyXeBwuI7w3slzI6PbCqtnKwzW3bdluQdJnKYYTkKC4RjkNwG+pKEEFkAV18mEASVOuuun1ffYh2nWkw/5PfNwtaci1cW1dcXLdxzlMIrFCHbLBgeK2BJkCux81IhJhbc7EWvobgkcdP5qdhDIXVI682A1B7wQtEcu7v8Ao/2qn7NQHLu7/o/2qnbRq/6F/wBjw/f/ABHrE9Nv7bn+5+GxSuEOldiVwYQ122zV8/dVsB7KzNzdxknieHAPgOKx4c9PD3y69IXKfpWdecOOC/i8RfX3blxinnkHht6eeRV0p19sg7q3jn2c38ZZt+hv4u6rt6HuDcA/p1w+ybyPg8XdYYp8sCVWQMx08MnQaa7axFRpX5dQtRSMzAk+CSmKweYZl18x1q092rYaxdAIuo5w9/ybNmu4a55yyC7Zb1sWzu+uyeZ/ZqwL+Kzc7o/zirD6iNPkayt2wcH/AMHX3sW7iXla5YZ8sFWNphcQBt1gkqY2lhrSWztO26JaZ5GXQj1CrPCuKOLGKRGym/cVb7j3jZt6raX7qs/jbzy2x9muXCYTYDKo+85IH0VWuN+gjH0rRXZJ2JNmVsTdtWUxCNdTJmM5mLKji6qKGguUIJlUcg6GnZwDsP4ZbHiIuk7lnUT8wc3+tQ6oazZNtpHyHU9ngPn0WefZp7TcHw7EG1cFwpislt8W+W3btsjN3QFmXfuiXOa89wNqhNq2Fc1sTmH+TPy/WKwz7U/J9i1jVw+CDMbuTLaAJOZpEAasV6hiNV11ADHWfZx3i8Lwtu9nF61aS1cDgh/zTm0CwOssqA6+dQ5AHyskHMXT0oyQPj/hcfn3r2uVwXa7bjVx3KvmLB1C471UPnfC2RhziL9vvFw5D2lzlB3rkYdCSpzAA3veAJDRl1q/XhVD554ddvYO7ZsgG5eNtRL5BlR1dwdCCGUx4iFBGY+6KxHTF1qqkJNtXXPd2LrddAwepqQP+n/nWdbt8E6AKPDCiYCW0CINSToqKNz1kkxXHwMxd03hspPnpB/bUtzRwhsLNu7Hfk+NQQy21GsFgYLkwMo9zK6nWRUTyviMt5H3AcSPSIP66lU1iwkbfFXtYe0FJcGshrttbjZULJnOkhSwDkZgQSBJAIPwOxYDcoWxnLXAiwTbKw+bwsABr4wXCwU6EgxEiO5j5VBYOnuOA2myNAzAnaGOomIzAfDv5T5SdydWhSAWJ0yyubL96JOxI0+VSBUBrCbDXTXge7XfXj3KK6mD3AlxFtdCLEcjcHlbTW3JenIuDa3isPMMrNazAGRlunu2RtjILMhBGsEiVIJ0J2pcOAw1x4GltAm8jJlmSZ8JZbDmI/kFmY1T+ItLZvgJqLb28kwCcrArPSSSWjXVjvTk58sgpBMi9cFttoCLbe2xJmSZAUAbMF9ajPfezig6OsNkpeXuX1IBuXFBZZRQyliwuBWRgJKMEzNmcKutuCZIFnxvLirYLlgLmZAi5g2cQA5y5mZRPiBzdGBkFYk8H2ei7bEQrwGVpZlgqJRzlHiV9RC6o0ATbLVGYjlO+jqjIxLaIQ4a2fgxjKNdnCn061IbNdttOd+Px28l46NpffMfC4t47fmvzlTFMraAnrAk6xpoKmeNJ45+9DH4kAt9Wk9PgKm+BcHNgKGIztdDNl+7btExm01W5cBIGnhG9RnMry0/zmI+BMgfIQPrSWuGfReu+yvzA1YuEYoqwNVvhr1auB3I0IBB3kT8/OPhr+NOP7wksPJWzAYnMDpp5k7nyGg2rj4xy8t1dRr+2uvBsIG0em3yr9xvF1tqSTTMQF0qQ6JccycJvW1hgmItAybd5Q408mILA+ROaPKkv24cur3VvF4S2VtHw31Xey6ZSoj3UWAQekhI98Gnxx7jV68h7q0zjWDKrPwzssj1E1Hdn3I7G3f/ACkaYgqXsyCqqsiDGmZ8xDkEjKFHQ1i8ThoGVLHUDmCbPldGwizgftF7WmzSwdrMQNRY3JAVlQVMsZ7V8qU6Y84exbZPGFt2wsZZZ7i5EJRRA8ZuMDEJuWNdWC4+fzi5wVgS5YwZ/OHcgZnzgQGkAM2oEV+e0DyZcshrllbj2cqLlQsSkxZce9mC92ZBAOUtc1AgUmv/AEuRg5YEd2HIChSGDlYZfEhDSzMRsq/ZOqrNfSOzHz+fNaWOpblB8PnxTHTjK3Q8sO6AIkauQmUljmiGnukUmCskDUZBUON82CXDqJFyVX7K5YtiARJUN4szASRqDJha8O5pYKEA0DHT7wOkmZkwB4SG6xlgzwcV4uDqpUbg7l321ZjMn18PX4B9mHHNqo7q9ttFacRxcAMQ2VYVQFmGcZCZkyoGsBvGAw8zUJiseuZ2JyAoM0TuSGjL7x1A0MjMokga1XrHFSVKkyJkbz69N9ANToJ869uH4E3iIPlJ9SxVZiYM+fSDVg2mazdV7qpzzYL9XM2on3h7olpbpIEnQaD6DertyTyG7ktdBChVMmAEJ/OEs3uqAse80wxMaKKkeGcFtJ3QymCbYa4YaQSAQoMAxGXVCpIMTFXzi2PLZgVhFLLIKqXClioDZmfxMTmca5FIAEykeap0s1SYqUXu7VdfJ3D7RS5bsqQEOYaaEzKw4BMArOYGSYIgaNY+EW2GYkgmQCSczGBlP9ESNBqCQ0TE1EYS6DlVmZZkslvMgOgjNlAISDlA97Zy0klZnhIIWNYJJBbQkeHTLJgRIGgMaxrJgEqzjZYrs5Ptg4zDiNrk/wBVWYfj9frUbxHluzxKyiY4XFvWDdFnFWwScjNIS6o8TBYAA8l95SWzdnLd6MVYP/vUH9YhdvnGu0mmh2f4FC91SNrt0fR2FQqTDo6uueHEtcGMLXNNnNIc8G1wRqHAEEEHiNlS43K6N7XDlZZ5XsJyse7x2GdOgvW7lth8pfXr5elMbkHkO1hn70RisUYi4UK2LMaTbUmWbyboNsmuZ63OVrZ6CurB8GRdgKvD0d63szzvezi2zW5u5xa0EjmBlvxuNFRvxF+WwACR1FFeuFwzMYVWb+iCfrG3zrkwBJsF1Vz2tF3Gw70mO0rixOIVLai45bIgOg7wsVlp6IApnoCfSofssxNv8tsOhHd27yszAZRcuA5Q8ROUKHK+iz11vfGuynG3L5uqbdqHJU3LkkQYUhbYudNYMb6warXOXLbYO8qFkBYi4O7S4AsmW8ZPiBACAZBlAIHvGt7AGsiDBvb5/l59y43MXPlc/mSfW61BzJxFL2HfKZW/ctWWO3huOtt9dCIVmPyrF/tG9r2IxOPudy+WxhroFoAKQHttKvqCAVKgD+jT25I4ubmGSwWhmvaMNgDHi6zG/rFNjmXkPBju7ioyNbw6YfwgOr2rU9z3lslc7WzLKysr+IgllMB/DpmROc+S2mwPO+/uvbxUiEtO5WBn7buJMT3t63iAfeTEYXCX1bRRqLlluiLsRsfMzyt2mO0ZsPYXaTYFyyTC5fdW4cPq3jP5nUyBlBga45q5Pwbz3tvDP0BbD37TRqdZuXhOm+aDroKU/MPJOBkhbdlQYmAx3Mx0YbdGFXBxCmf9to+fBPuY0fZd6fzVa5UxRuotwH7Sg6gwQRKyND+0H1rr4wwF1p2Ua1PcCwNq2hW2oW3bzXXMQCd9BJgSB1OgGp3pNc1c1Es0faJP7qrYouvkIj2TNlzc+YmZ9TpVNU11cRxxfejhuGzmOuv6pj5xWmgj6mOxSXFWDlu0lxlDkW0JUO+Q3Mo+9kUhmM6QCJ6xqa0n2O8q5LQuvqr921lcoQlF8Vu7dVXcG4xbMFLMLcJADTGbcHYIFlxmD3HKLbVS0qMqgqRq7FmjIBPu+YnWvZdig+CwxClALSIVII1QZGIkAkFlJBO81kelMz44Bk2JsfXT09yu+jNPHJUkvGwuPG4Horzy9u36P9qp21UDwA6t+j/aqctGtr0LH+54fv8A4j1zzpuf99z/AHPw2KRwxrutmuDDmu22av5FVU50WVe36zL8VtEe5cXEW/UNZsXzH6TXB+i1Z84TxW5aMoxU+lah9pHC5cdJEpew1iR94q2JtOD09wovwJ9KzNg+X3a9ctIVJTUZiVzLoVI0OpUgkab1Hls1oJ2WnwyXrCWDfRTg7QMWRl71vqf31B8axrN4nJYiCZ1rtHLd+2SWtnTYiGHx8JJ+oqI4y2hBkGDvv9K8Y5haS2ylSiUSBrgQF/QPFcL7yxYs3C6o+DwtnMu6N3Sst1CdM1u5DayCJUggsDlfmLjWMwt+7Ya6yvadkaCQGgwGHWGEMAZ0Nbk4FftjBYdrrIiGxYlrjKq621jViBPl1rNPbZ2d4rHYzvsHh3uKyILl0m3at94ua20Nee2zqVRHzW1cHNoSQYr6WqYHuDyAO/54oxCjkfG10QJIPDl/JUnsK4gfy9bh1fxXGcjM5gjdj4onWJ8q1s2MLB53JE/Ekkj9H3fkax92Q4a7b4ncsxbIwqv37qWcZ1IRLasQgkXipOhkWrgExNak5Yvyjendj694f2VIeWySsc3ZQerkgpZWyb67m/cpBq8LlezV43DVw1Y2Rct2oPhcSBO+ka5f0o12k6DfL6ipu6a5OT8KHfKxhY18jOhBMgwBJA2B16Vz/p0CZacDft/5FvegTw2GqJ/g/wA6zn7QmHK8QDEAZrdttN2YFiWYRImRGbxEKSQKX/CR73o364j8VNMHtTvrdu4hO5S3iVbE37lpLQtMf+shLdsgAPduWsJZuYo3NQ4vSuZVBpbYG5BMbMIPXrofiPP41Pw+IsgDDwACuamTO4OTg5E4+1tl18LaMDJBWIJyzuASRGsgb0wuVbo8eX7eVlnyF3ESqqAFAGdTt4Q40JMqm+XtUU9RG3poaYfIuMy3NTAHX+aRO++npJMRvvaNbHNTuFu20e8D/W3Ow8FRz9bDVNdf6snW+wJH6jwudeK97j2reLbvXAdb1oBWByqAqXMztEAFmA1IyhGneVbfPHFsMow7PetgqrQMwJGcqcxVSZWAw6gtlj3ZCE7TsRbbFXGUyHKMOhA7u2II3HiDDXoJ2Irg4BjMrAlVaOjDw7TJGk9DB0IjeoJhuLqxa8XB+dVp/kjicWw0i5aYsqXIIJCkrGoAOoPlrPrNixmPSJ+fxjXb0iZ9KW3JHaGlxFtOtu2AqgKqhUPnCiApBDEAACI9TXxiMUwN62z5grBFPVkJYydTJ0KTIkAzMmmLkdkeqMgc65/1Vg4xjQz5o1AZR5eIgnTadN416zURibeYVzi4Ykn9/qajOI8fVZEgaa/DUaU5GCnXEAKTwDVZOE3gBJO3kdZG4H1DeR1HwWeA4tduyMPae5qdVHhB8i5hB82FSXdMgP5Rdgk6WMPFy6SN1Z9LVsn1MHowOlR6zFaeJ3VB2aTgxozP/wC1tyPE2A4kJtjCBmOg5nQe9XfGc3FjktBnb7oG3xnRR8SBVe4xiWEviriJbQZjaVw11/JSBoFMamTsRp7wrXG+ZiiG3bC2kP2ELFzsCXu/yrt5wB0EtBJpfHsSLuiT3okgLqXQQeoC5gNc2aT0WCRbI8Lr6lmaZ3UtP7Le1Jbjd98rCR+6HkcH32bdUtJswX7zt5Dc+dvBfnNvMS4nEI2Kti5h0uowtFmUKEYFcjKVKmNCwgkMwMrodUYDEhrNt8PkyEW8gPhUW8yhgFUaMqZgFiAwAMa1i+7jGaQdDBnfWNo6nxDbWD+F+7G+1D8kf8nvsTYckg6lrbSBIEZip0zKANMrKJkPZVOHxiFrYGhoYLBo00HD538V7SSmJ3bOhN9ddVoPjVkOrAgEHcEAg+hB0I+NZw7VOz/DjMVtBZMkp4SDEEiIGWBGXZegAJFaKvYgEBlIYNBVlOZWU6ggiQQehE6VQudbYYGJ9R+7r/y89KylQ8jW9itpSMBFjqFiXmTlTuHYGdDAPz00Pn+/yrht8tu4JUqYnrr9IB1nqB+NPPnPhGYFWUkdCN9PXaNhHT5CqPh+SbpYFTGuhBgj9s6Rp84qXFW3bqdUzLQnPYDRL4cGdd1P8fu9KmOW8RkOUACfeIBDRGoDASJ6nT9VMa9yVfVd0fTb3Wg/6p6aR9air3BSPeUiNf40I/j4166qzixSW0hYpvl3hIJDgnMAcoJkCQRoDoNGjTzI1BirTZ4UNFZJAhpg+InqXzDRYBMmIhdjpVuVXVT1kxrr0026frphWsdCgiSYggbsJOw0CkACCDOrR0qASb6qyjYLL04dh1G58U6sZaYC5QCdxDEKNVMtoSYMjZbPrroPKPT4En3tBGx6LXLgbcHMpOUKxhpIkjTUjQiMoCgQD/OAEkLUL1Eyf432FeFSGhRaPFxG6hgw/Rg/uq9cG4q7P+U4ZWu2bzHMFgvZvKSl21cUGQVdTrtv0ylqKRLD+Oo3rPnMnHnt4rGNYu3LavfuK/dXHt94oYg5shWRnzMCZgliPeM+0dJNJP1tO4Ne0EdoEtc0kXBsQb3AIIPAjYqkx8NyNJ52+fnit6YfnMqQLqsk6eJSAfgdj8jVo4fxlXGhFfzu5R7XMfgnA79sTYb3rGKJuow6gM5L29NihAndW2OguTu0/BXsptYkYO43/wCXxWiBtoTEj83lJ2DEsQR4V90XH9I1FKbVkfZ/fjDnN+82xczx7Tebhssq+EO+wfI/Nk3OG8qqgGcC4/Wf5NfQLpn9S2nkOpnLPC2IA6bADRfkBoPpUziryJB061B8Q5rVRpGlUEVDT04tt4cfE8U5VVs1S7NK6/wHgOCkBwZV36VVcbwfC38RbS9bS6FW4wzCYgDaPj8NB6VXuce0KJhvxqsdlnHmvcQXyFq//wDbpXk0sYsGN0uo/eqibiYXFYjDMzRbuHLJE5NGteLp4WH6+tN3h/aDhWsA3hqqgBR9oAab6fPXp51D9sfJtrFqCFAxAyrbujQ+9OVuhGp9R0rKvaNfxOEc2bwKkSAdYb1BOh2pqGn682bunPBaM4xxzDuBGjNqPEQB5a6jYgGPpSw5s4nhxcYKxIGmhBOnxHxj4etJR+cXiJPxOunl/wAqiOI8ws3U1ZRYO++q9DjayvHN/PsW3tWtA0At9oj1/joKVl+6TX5qx6k/Wpzh/BoEtv0Hl/xq7iiipW24pY1VfNTnKvB0uFs9+3ZC5PCwul7uZspFoW7bpmX3j3jW1A1kxFcmM4cc0DXrHpXgMI0xqKkl4c3Q2Xoatcdm/ZZYwhW4Qbl4J7zmVRmYlu7SAq6ZRMTIJ0mmJSg7COfw9pMNiLjd8ult7hkXFPurn++vuw2rDLBJ0pv1x/GBUCpcKg3PA8CO7uXScJNOYAYAAOI4g9/epLgO7fo/2qm7RqD4Fu36P9qpy3XZehf9jQ/f/EeuE9OP7cn+5+Gxd+HNdts1G2WrstNWgeFTQP0SW9q/h5jCYgTlXv7Dx0Nzu7tok9B+auKCftOo6is3YfERjbL9XBRv1jz8x9BW1ucuN8Pazds4rEYYW2Ui4r37asOoIGfOrqQGUr4gwWNYrBHF+IqL02mZ0t3GNp3AVmQN4C6iQpZQCwGxJ2qLUWdEWLR4OHCdsgGg3+KaPNXH0sKuaSWmFUAn4mYAGo+dU88Ta8yqLDFJzEG21zMBJIyIDOnkQRvpvUjw7i1u+2bLLKIGYTHw1jXz3r7GCbPFrDi4zToyZxJ850BJgkkgSF8qyo+q0I1XTGtE1iDp4Jw8L7Sr1ljduYO+toWlRblyxflVS0qR39zMwV7gLCSAqkaEiAyuwLn+1i2uC25MAuVbR0mTDR4TMEgqSD8QQKv2S8NYJlucPtWszZk/M2HUdAZtgjOBoWiTO+lePAeF2+HY3FXWdFtXrbuiAhWDZGOU6RDPKpMaggTBiidOwuO9xa2u/wA+KspIRkLBbbgqJ2EJKY2+fexGNukk7lUll/1rz/wKf3JbfmmPm8f1UU/2zST7CeFk4a3bUhe8uO+dgIXvXOQAAwzuoARQYYwOur94dw9bShFBAE+9qxJMszHSWYmTsOgAAAG+oWZrHkPyXKOkFQI2uj4uPpde7GvC41ezmua6auWhYeQrxumoDB3SDpGuhmCIIKkEHQggkEHQgkVOXDVcWuedP9H0/wB//IujfRzZzKkH+D/Ou3mnlDD37DXLyOGs22a1ftM35TYKS6my5aZzL7rkqx98nVqyRx7EozZld3Ysc2azatDfcG1cYMT1YopJknUxWy+GcbAXK4DKQyupB8amZ1nfUgg6EMdRSB7e+Rclw4ixbvNauB7ty4RKpqAS7CZ2aW8OULLD7TVfR2uAPVyu1JNr8O7XYHfQ76K9r6d+Zxtb8/niqZyvjo0+Ma77H9XSrNxfind6A65Y8tCSPpv5kb/Fe8Puxp8j8Nf1z+qrjwLBd9mY+IgEn+dsF9PPT4VrnAMdmVZ/WCy5sFdYnwqWO+on5n4/rqz2eM4lhlLXSp+yzXCuvoWy/hXDw7FopKupkGFZWykakxoNZ/Dy2idbm2wqHxXhoB76EE+9JIVTGYe6dYnXWKUX/wAKd9n0uJfW35LzwuLMwwgj0htZMHzk9TPximHheLIAlw7lMgB+9bUEtM/aZrjGdeu7QKlyHhb+KV0t2SLDBj3ryiK5KFWF5hmuEBACFD6HYZiatmE5Gw9hUGIu3MSw1Fq2clsZo3I/OMJEDxLM+7VZUYnEXmGFpkkG7WDMR/eOjWffc1NZTF2nu05nj+Z8gua1j72Jc28MufLoznS3b/p3DOvWNWMaAwa78Jy1h7ZDXW/LLv3fdw6x0yiTdjbUkH7qmu7iPHPCLaqEQSFtWwqW1A6bCW89QNdFkE1TOL8dYyeg+nz18th8vI1Ip8FqanWsdkb/AMuN2p/vyCzj4R5R/E8JiWp0+rH3iPgP1v4BWjjnMjZYLAIBAS2AqKB6DTKonYj41QOLcyH3kgwIWCBGogKRIyztrEhupM1vjXG3M6kGJJGjgCfMydB0gCflUemIYgiT4vidoGwDTI6bRIAmQNDTUcNJH1dOxrG8mgD323PedSo8kbr53G/jqp63xJnBzD6kDQkwYgDX5EwPUjlXDM05GJI0BBBXLvrvOkaLOnma4kw8qCNDAJgZjIY9fUxIA3BiBIqNxS3LbSGPyOmn6TSBtv6mNqkZUmynRZzZpzd8JDqQQqgaArLNcOULr3ihSWAZs+QNW+Z8IyyPLyOo66kwSRI1jSZMSDUjh+KG4At4EwQFfXOsHc7B0mMyZoOWRGUMO7/CKuZusFMFu8bKiHZp0GRbhLDVsziQD3kswjOZZPg6C64+z3tdxGBlNblrMZtPmCjfMVPvW2ZuqqZOrBogO3lvtGweOGW2+S7sbV0hWJMn820ZbgPQCHM6otZ447y2TqZhsuXKDqDJkAlcwOupgSpjwwTUL+DKk6fH4GB67ddfrVPWYfHN9oa8x86qyo8Qkpz2DpyO3ly+dFp7mvBFCdNBv5j6j9cddarlmJHT+NfID4fqmlHwztNxVoBSwuoNkujNA00VwRcUAAwubLqfCdIl+H9qdkkZ1e15nW4oO+6jP/qt0+NUMuFTRns6ju393+q1NNjcEls/ZPft7/1snbgbeddROo+XWfp1FR3GOFA6MPgf49K5eS+csPeAFu9bZvu5srj/AOWxW5ufu1bcdhsyxEH4afj/AB+yO5jhuFPErH6tII7kqL/CRbfTadtP1z/E1ZuFWhA38v4Oo1+eh6V68ZwbdRt5H4fx1r44VMbDT1j+P4jpTdjxQ1wurJhrmw2/5/T6eddNx/D+z08vL/h8KjcOdeg+ER8K98Rc6fx/Hy9KCnVSe07mT8lw73B77Slr+m2x/REsfh61nPLCROp6+ZP7abHtCcwWXFrD2yHe1cNy4R7qkKyhJ6t4iSPs5QDqYCmxD7D1H/D8dK0OGQdXFcjUrH4xU9bNlB0Hx4rxxzEsg9Nfrp+FdLjMGXy1FflwAvMaKoHzUATrO8Sfj5aV8cLvak/GrJVC39zFj7g8ObcwszGY+Z/Cao3MeDxmWVQtP3TP8bVMdqnGwiNESTC+YC/8Tv5irN7OXHWvWLi3AWhiM3ULAj4eXyFYRsYc8NumwEk+E8mYvEXACpEn7VPnk7s/t4RVfxG6FYExp4hG+2nlTCwT5SUZQSZKnSW36nqaqHPOPvbHwCYCjc17LCIo8xNz4beP+q9Om6gOaGgA6yCDIjSD112qC7WeD4fEWJvWwTEaiSD5g66HQg1N80v9jqV/ZS85p4uv5FdtMSWQwCDqFb3Y8wD5+dRIrjZLaVnTinJ9kMYECTH7KibnK9obgn5wPoIq43Na8FwM1bMqZB+0U8CqzYwSr7qgfAV8X0qdxWEioHjLwIFONcXleErj5eUG8xI3gD5f8auN7ltWExVT4LaysDTM4HfkUVDznuE04ql4jhGXSNKaXZh2hkRYxLEjQW7x1I6Zbh3I8rm/3vvVEcTwIOtVjHYSKh1NPHVMySDwPEeCl0VfLSvzxnxHA+K1ZwNveMiAASZ0AGYkk7ADeTpXniOdsGiG42Lw2QEKWF+0wzGSF8LEyYOgE6HyrHvP3NF9rFrDNcY2ZYlJ0bLkyB+rBJJVTIBM7gEUNj8K1/R7NR4fHBobZted3uPluqfGsMixKukq3EjNl000s1rT46jTbTgtRdpntHC06pgBZvqBNy7dS/lmT4UQNYYiIOfMQdRA3Kt7Tu3DF460lpsuHUZu8GGa4iXwYA7xWd7nh18PeFDm1WQDStmip75nO3TtPh8EIAa0acePvRbMbadDHXrBjfaflX1XzX7TYKmL2wmKZDKkg1feXuf+7s3EK+O4mWSAQZhgd9GG4+Q61QWsmA3QmPgfI+sajzE+Rr8sjUfGkyUrZdHBPwVj4O0wp+cH7c7lvD+Fm75QEtkDwgnViSTIJ91RHmY6Dn47zQ3cd9ig/wCfnIgAW/fYnRLJMvbtqdGulDlB8IZ7iqIblzmUWbbuFtocupt20Q5RByAqARJA8IOpkmfEarPJGNbEY78qxDSLJ75i3ugpraQfzVMED+bruTUJ2BxteO83+fBL/wBp3TxOIFraE9/81qviVpMJY4dYYIty7fW5cC6CMHYu4zEMgJJyW7tq3bEk+HISWYljnzAdrmIwlru7WIZzbwnDjaLk3Uz/APaL9soxKxlxTWTsw/J0AIKKRfu0GziLl7B4i6WS5cwfGbVvDkZTh1PDMQMLnBGYXsQe8uvMQO6UqGttKW7OOFJjMdhLZGW0RY7+YgJh7S98x6ZclouZ6ZutS29kn55qvID26jdbYw/MFs51chLtqyl28gkwDbzvkiWbKQy5T4pUDUkV64LGrctpdQ5rdxVe20EBkYSpEgHUeYrNmC57LnG4hrndYe7euG/eJILC4SbXD8MVBcM1vx4i6gLIoYpB7ovYOVe1O4hBcpatOQZxJFrwwEQWMFaV3SwiKq21e4gySfG0tUyCukabOF/iqKtwCGQZozlPvH8vf5J43GqAWuPh/aPw+5lAxloXWMZGV7YLEwFQE3H16Bt/PWuy2uwGpOgA3J8orG9OpmyugLf4/wDItB0DoZqT2gSjfJY8D9tfQrssY05cp1UzIkjcZTqCDrp6iBBFeWS0pIu4izZKultg76i48FbIAnNfKkP3Ck3FVkLhA6Zlp2gdq1lLSrgvzl57Ss954NqwzKG7u2sBb9xSYNwzZEDw3DmVaCjwKrm/ZsOZ09N/RaWtxSlaCCb+Gvrt6rz7V+zjDph3xVkraKtGQuQLrM1oBER58Sp3tyLbHTJKruFTwziptmQYj6fOrX2jc1NfKgk5ba93aWSxA+0xJ967dbxMQAPcUABFFW/s17LFshcVj0DXYDWMG2qprK3cQBMt92ydB9qW0ta41fs0TYXXkkdo1o+0891zoB+04mzRqSs29upkvYbm/D3cfDdcHK/Il7Hqt24PyayZIvOCXvfa/NWdGubk940AgyM+oVjctcr4HBkd1Z7y6Ne+vkXbgjSVXSzbP9AKSDqdIPVxTjDMZJkkanf5AbBQY0iNJgE1BcUx+XX01Gk+XnJkg9RMmpUOAumF69+b/pNJEY7idHSd+azT/wAsJh0znfYGUc/2v5eWverRxPmpzMuNN9t+h6D1gAbHSqdxLipLTqTvO/p6yfgY+YMQGP4kCTJ31MkAGCdZ2gfSCNZInls4yOuYSYmSNCvQkQIHU6eWxF7TwRUzMkLA1vJoAHuFgo+Qud2jc8zr8VLXb7trpG0zMxrOYHMSNiWEeHeRpXOKXSvvFND6SC07SuoGm+8H1J97PEQTudv6IIH06wCTvqTprRi7KlQdBsNIAzfWPLWCYkbxT4TuYDgqdj3BP0AGh0JmZGogwdhO4jYTXAMOSwGYCB5A9Op1gbaLoQG9a8ro3dYkHMplWMAmDlkMdfCRqQS8qCIPJf4uA0FCpJIhQux3AIEaSTOXrtoa9Tp1Ct3EMMNBoTIk7SIDEyDJEMNDrt5Co/FWTAVlSQfWSTpAlwN/vemqiCPvhmYqcxJj7JMg6t12InQR5HfevsFQdT1JGuh1jKJ1J8MzDAEHN0pQOllGcLGy8PyBtcoAEanxCA0xMwQABMzA9Na8TwEkFWVYjXMJ3iTrJ6eXTqBrOnEKDpoBmIBygjUGSfFEdF+IljIrmfFIDPU+ISoE6GSCBmGzAtuQNfu0aELwnW6gsTg8RZlpXEJqQpZCymRlg5hcCqQfzcoCB9YN7mGumGJVtBByofsnTMAMoVX8R7pUGQANlysyrHE2kwyqoQHRQQzQNSxMlz7xOY6s0ZYAqE4rwS3cBLIr6TBDBpOghluhk2mcoO0kBhDHVX2TjHapbcW5ReDlKOsnLkjU66KftGQF0zeI6aZiKNxXh7KTmUj4iP102cTydbXMbNy9YMkQzd5odFBJCMZkaiemms1wY7guIAhRh7yxKqvgcbDKEEW9fIkzOu0VFfTnknmyNPFJ64lT3L3NuKw8dzfuIAIy5syR0Hdvmt6dPDpr5munjlsAw9l7ZEzCgiZ6ZMqr5eWg061Auq9Dr/HnFRXs4FPNcRq0q+We2LGwA5tXPV7eVv8A+lra/OPjNSOG7ZXG+HWf5t1lH0KMfxNK/uv4/jSvtVqO6lidu0KU2tnbs8/H4pqv223I8OGQHoWuMw+YCIT8mHxqvcwdqOMvgqXW0pkEWQylp6F2d7gGvRhPWapVpTNfqgivWUkLTcNC8krqh4s55XRbaK9bG8n5fH9XqflXhaXz/wCP0/f5V695qPwqTZQ16ltD61zYY/WvsNXyUpSFoXmPir4m8EQFmdsqgfH9Q3rW3ZbyyuFwqWQJYrmuk9WPSfw9K/nryF24YnBubluxhHc/aupeYj4Zb6AfSr83tl8U0/MYDTb81iP/ADUfPf1rN02GvYbuXgabrc3GLeZNJzCWBWTHnrvHwkCom26XAS4/OAa66MNs3x9Kxbc9sfiZ0NjAR5d3ifhP/a9T6majb3tXcRJnucED6W7/AP5k0qegkcbgBeubdaO5r4ifypSASqmD5QdNaTvbPiDbvso0DD8JmqM3tNY7X8xgtevd3/8AzFUznLtVxGLcPcSypAjwLcE/HNcbWoEWETtPat70NaQr1wpS5AGpJAFWniPB+6TxbkUleXe0q/YbMtuyx6Z1uEf6txa6OYe1fE3zLLZHoquB+Nxj+NPnDJ83C3ilG6tnE79VjGat86r13nC6fsp9G/3q8f8A0mufdT6N/vVIZQStQUwMPgJWpfgl8jQ0uMLz5eUQFtH4q/8Av0Hny9M5LQ+T/wC/SHYfMeXvSMpTxsXpFRXFsPSts9pl8fZtf1X/ALyi92mXzuln+q/95SRh03d714GFe3aGkNb/APmf2Kq1e3G+YnvEFggyzGUHrE7sfuio/wDLD5D+PnV9SN6uINdvr8SnRsuo0A1yflR9K+jjm02EeQ+evn856DYCn8wXq78RYiNQQRII8vXyIMgg9R1BBPnFeWJ4pIWERMoMlc8tJJ8WZ21EwCI0gGYEeX5efIH6/vpYe1eKw8oXrfei3e/kbsW7jazbzaJeGo1tNDwdGXOp0avbjnAruGvPZuCGRiDGoMaSs9D+qqt+VnXQa6df3/L5mrJzbz9exIt94toNbt27RdVcNcyDKHuEuwLkAAlQoMDQU5FM0bpiZjj9njv+q8eIY1mUJM9BV9w3Cb+CwqXgCveLbu5xIjvlY2wbohkfIMwFoqwETc8ZQKe1xBgZgEwRrOkgrO+4mR6gb1aMX2oYxwytcQq9tLTIbVooUtgC2O7ZCkrlEPGbTelipbmzFMOpiGhrdr3KYtrni5d4Ze7xhbvWLtp8Hdyqveam3dtJPvuLd24ZAbRnzETJpnJzFbLW7aOb2KDYdXXLC2iVbEqMxHi7lba5iQi27+ILsoFVHEcwOwOZUZmEZzmLgeSkuQoHQAADyr3w3Nl1CpTKhVBbELIyQ2ZSrFlZbjM1x1YFXZjIICgR5XtJuFIia4CxHgmPhcJdvBDZNsrZZ7OGtW1fKGUC4y2br2sj4m6CLpZslzEHS0zkJZWHtcHkd5fuCyj+IFsz3bgPVLRJxFzNp4myWjoTcWqtwvnF7dzvFt2s3hyfyqhCtxLoIFu6gYhkBAuZwDDABgrDmv8AM9xixISWYs2hEkmejAabDyAFLilaNCkTMcdWhN/krnr8kYLgba4fOyi5irqpexbpmErmKmzZtkaG1ZQsdzcY606OYu2Ozh86YFCzZHUYq5lnvDCqUSCBaXxMdQzEWxsS1Y5tcz3B9lPo3+9XVd53vH7Nvz2b4/fpmohppXteRq29vO36fITcXtQa5t7A20vyv+quVzEeNmBJbNOZiS5MgksxliWIzMSZJJJma+OJGAY0Gsf0d1/1SDVHXmq55J9G+P3q/cTzXcYQQm0SAZ02+10ED4AesudaEk0zloXsQ4XnduIXknDYUFkna9i/dt20B97u3OYtsri1/OysvCcx5iWvnIzkaNqja+FUuCbZJJACsVdiICaDNmPF9ueKa1Ysi1hVs4dFS3aVLuQZQRmIa+xZzMlyZJ13Jn7bt4xhVlNvDEPvNu5tAXL/ACsRHprJqDhMXVPkqZv6x/ZAGuRgJs2/EuPaeRYXsNQwEvzxukc0cB6nn+Q/mtG8TxmaSNBGnwnTT+dpuZ01iBVV4ziY0noTuBsTIMGB9oepIjeKSFntpxYEZbB10lHJUfdB7yY9WltgCAABycQ7WMS+6WdiNFudYn/K+gHy+NXHtTLpQYU1sbizqdonU9DMiGhVGmmoJnMdJGXzwvEJjaMpB2G3XUSdCNPKBSbvc/3jrlt+Wgfb+vGv8bmfrD9oN5dltfRxPxhxXjqpvBKcy+qbK8Qi4VETI3VdTqxiSEOmmoPTzMyrYvRo89N5nYToFMxliIgHTaUZiee7zNmi2CJ2Ddd93O9e1rtCvjZbfQbOdtRu/nr6fIQe0sO5RlNlfsJx1zmBK+8QwKwAR18OktAGixpEb14niviUMGEQFOhWBsJAUny0jSN9CVsvNdyWMJLGT4Tv6CYrtTn29ljLaMag5WBGwGzgaAQJmK8bUtAXmU2WgsHil0ImIHmuuhIzEGGzA/aIkddDXEbozGZGaeugk6SFJAjQDwnQSBpSYwnajiFAASzpqDleZ857zf8AcPKvs9quI/zdjr9m516j87pqJ8Ma+dO+1xpoQusndctZQHneYLIfEBI0zJJDFW1iIVh4thyW8MpM6g7+FRMzuubKDBH4AaATSes9q+JBkJZ/quNPLS5ppI+fmAR6f9LuJ6JZWYBKi6JA1A/lYgfDpJkzQKuNedSbJ1Y0SJILMQPux1gBFBUaDxFcup1HiJH7gMWwGhIBKzDGNz73iOszqdBFwT91Kv2v4kmSlg+QK3CAJkDW6Sdh70kxqTrXxc7WsQf8nh9NvzZ9BvnzaAQNdNIghSE+1MR1Lk9FvA6xJUnZQJPQz7oGh11aVAg9axxVsrAag6AKJkkwoXTfU6QD0HlSpHahiZmLY0IAAuAAa6fymoA0AM/rryx3aTiX97Id9fzhOvSTcJiNIP7TIatiOoPFXnG8TJBDDMD1IBMbAaSRoo02EDTzq3EOBhpYER6gAjfynyqvPzfcP2U//s/vK8rnNFw9E+jf71RnyscnGRlq6L3Div7N6+TaPn+FcN7jrnov0P8AvV5f4WbyX8f30wSE8L8VILb+H0FfTAnr8YqK/wAJN5D8f30HiTeQ/H99elwXllLoIr8AqL/wq3kv0P76F4q3kv0P768zIspcCv0Cob/CjeS/Q/vr6HFm8l+h/fRmC9IUfRRRSF6iiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEIooooQiiiihCKKKKEL/9k=", "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "\n", "YouTubeVideo(\"W4Mcuh38wyM\")" ] }, { "cell_type": "markdown", "metadata": { "id": "6ttDW4giJCvY" }, "source": [ "这是一个使用了[这个模型](https://huggingface.co/sd-dreambooth-library/mr-potato-head)的例子。该模型的训练使用了5张著名的儿童玩具\"Mr Potato Head\"的照片。\n", "\n", "首先,让我们来加载这个管道。这些代码会自动从Hub下载模型权重等需要的文件。由于这个只有一行的demo需要下载数GB的数据,因此您可以跳过此单元格,只需欣赏样例输出即可!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "MwT1_nk7gfpt" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4a32c40a421a4cc7bc45051a11e8515f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 15 files: 0%| | 0/15 [00:00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prompt = \"an abstract oil painting of sks mr potato head by picasso\"\n", "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n", "image" ] }, { "cell_type": "markdown", "metadata": { "id": "EzIWYp5k0ngI" }, "source": [ "**练习:** 您可以使用不同的提示(prompt)自行尝试。在这个demo中, `sks` 是一个新概念的唯一标识符(UID) - 那么如果把它留空的话会发生什么事呢? 您还可以尝试改变`num_inference_steps`和 `guidance_scale`。这两个参数分别代表了采样步骤的数量(试试最多可以设为多低?)和模型将花多大的努力来尝试匹配提示。" ] }, { "cell_type": "markdown", "metadata": { "id": "s09TpWIAglM9" }, "source": [ "这条神奇的管道里有很多事情!课程结束时,你将知道这一切是如何运作的。现在,让我们看看如何从头开始训练扩散模型。" ] }, { "cell_type": "markdown", "metadata": { "id": "3TWKkNlfsATE" }, "source": [ "## MVP (最简可实行管道)\n", "🤗 Diffusers 的核心API被分为三个主要部分:\n", "1. **管道**: 从高层出发设计的多种类函数,旨在以易部署的方式,能够做到快速通过主流预训练好的扩散模型来生成样本。\n", "2. **模型**: 训练新的扩散模型时用到的主流网络架构, *e.g.* [UNet](https://arxiv.org/abs/1505.04597).\n", "3. **管理器(or调度器)**: 在*推理*中使用多种不同的技巧来从噪声中生成图像,同时也可以生成在*训练*中所需的带噪图像。\n", "\n", "管道对于末端使用者来说已经非常棒,但你既然已经参加了这门课程,我们就索性认为你想了解更多其中的奥秘!在此篇笔记结束之后,我们会来构建属于你自己,能够生成小蝴蝶图片的管道。下面这里会是最终的结果:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "4a99584eb73440e5be79572549490704", "23d6333cec9f4ae8a89aaf3dd2be3fa6", "ddbe46751fbc48bbb5385ebfeaf62ac6", "4517c381c26d43cd8d03fb27b1a26774", "295fb8ff81a742e29db36e10c7f0c9df", "40dedef6535d437591ee8e41c51473e3", "e670878b67b5487ba0648b417f0c6801", "1943d545cd5a4b2a80fbe75c2bb0faba", "8d4c6bb0c2f24a898f38f51aba4c7b82", "72c1e8031d5d44229bf6d1c989f628fa", "77a61fe86ad04d448019ce8b6e7e48bd", "d7510d76ac20458ca4df34eaec865372", "b2e83fc209494d47b966e0abf071dcc5", "68f358d320194bd1a7e8ce64e26f7bb5", "9dacbfae20c54687976d30129253b0eb", "42a7d07aaa6e4a50a9a7efb2de9cbf57", "9424e1b1c56941aa8a7b3077015f72b8", "e54b6068e48d4b76bffabefe0d974ecc", "5ecb9b67a77f418c8cbc5245941298c6", "9cc9689be42246c48698067ddc6fa5ef", "2caa903e553c49d1b361b9265a1910bf", "6d70ce5841594677b6cf568ebe893636" ] }, "id": "SJEiaGNbKI6T", "outputId": "5bd30a67-9117-44ee-fbb4-338580cab3a8" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "78edc967972b47f498ae47e1b0a25f5f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 4 files: 0%| | 0/4 [00:00" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from diffusers import DDPMPipeline\n", "\n", "# Load the butterfly pipeline\n", "butterfly_pipeline = DDPMPipeline.from_pretrained(\n", " \"johnowhitaker/ddpm-butterflies-32px\"\n", ").to(device)\n", "\n", "# Create 8 images\n", "images = butterfly_pipeline(batch_size=8).images\n", "\n", "# View the result\n", "make_grid(images)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a9_OpC7EKd7W" }, "outputs": [], "source": [ "也许这看起来并不如DreamBooth所展示的样例那样惊艳,但要知道我们在训练这些图画时只用了不到训练稳定扩散模型用到数据的0.0001%。说到模型训练,从引入介绍直到本单元,训练一个扩散模型的流程看起来像是这样:\n", "\n", "1. 从训练集中加载一些图像\n", "2. 加入噪声,从不同程度上\n", "3. 把带了不同版本噪声的数据送进模型\n", "4. 评估模型在对这些数据做增强去噪时的表现\n", "5. 使用这个信息来更新模型权重,然后重复此步骤\n", "\n", "我们会在接下来几节中逐一实现这些步骤,直至训练循环可以完整的运行,在这之后我们会来探索如何使用训练好的模型来生成样本,还有如何封装模型到管道中来轻松的分享给别人。下面我们来从数据入手吧。。。" ] }, { "cell_type": "markdown", "metadata": { "id": "QOvw1ej7suWg" }, "source": [ "## 步骤 2:下载一个训练数据集\n", "\n", "在这个例子中,我们会用到一个来自Hugging Face Hub的图像集。具体来说, [是个1000张蝴蝶图像收藏集](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset). 这是个非常小的数据集, 我们这里也同时包含了已被注释的内容指向一些规模更大的选择。如果你想使用你自己的图像收藏,你也可以使用这里被注释掉的示例代码,从一个指定的文件夹来装载图片。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-yX-MZhSsxwp", "outputId": "f8efea0d-41e6-4674-c09f-d905d6cd05dc" }, "outputs": [], "source": [ "import torchvision\n", "from datasets import load_dataset\n", "from torchvision import transforms\n", "\n", "dataset = load_dataset(\"huggan/smithsonian_butterflies_subset\", split=\"train\")\n", "\n", "# We'll train on 32-pixel square images, but you can try larger sizes too\n", "image_size = 32\n", "# You can lower your batch size if you're running out of GPU memory\n", "batch_size = 64\n", "\n", "# Define data augmentations\n", "preprocess = transforms.Compose(\n", " [\n", " transforms.Resize((image_size, image_size)), # Resize\n", " transforms.RandomHorizontalFlip(), # Randomly flip (data augmentation)\n", " transforms.ToTensor(), # Convert to tensor (0, 1)\n", " transforms.Normalize([0.5], [0.5]), # Map to (-1, 1)\n", " ]\n", ")\n", "\n", "\n", "def transform(examples):\n", " images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n", " return {\"images\": images}\n", "\n", "\n", "dataset.set_transform(transform)\n", "\n", "# Create a dataloader from the dataset to serve up the transformed images in batches\n", "train_dataloader = torch.utils.data.DataLoader(\n", " dataset, batch_size=batch_size, shuffle=True\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "-T5nDp9XN4FR" }, "source": [ "我们可以从中取出一批图像数据来看一看他们是什么样子:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 98 }, "id": "IEjN9CJpcrxd", "outputId": "d4e57420-d4a5-4162-a5c9-42106209631e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: torch.Size([8, 3, 32, 32])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_4278/3975082613.py:3: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n", " show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xb = next(iter(train_dataloader))[\"images\"].to(device)[:8]\n", "print(\"X shape:\", xb.shape)\n", "show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)" ] }, { "cell_type": "markdown", "metadata": { "id": "FSPVe2DOOHXq" }, "source": [ "我们在此篇笔记中使用一个只有32像素的小图片集来保证训练时长是可控的。" ] }, { "cell_type": "markdown", "metadata": { "id": "ZUImroTysoxp" }, "source": [ "## 步骤 3:定义管理器\n", "\n", "我们的训练计划是,取出这些输入图片然后对它们增添噪声,在这之后把带噪的图片送入模型。在推理阶段,我们将用模型的预测值来不断迭代去除这些噪点。在`diffusers`中,这两个步骤都是由**管理器(调度器)**来处理的。\n", "\n", "噪声管理器决定在不同的迭代周期时分别加入多少噪声。我们可以这样创建一个管理器,是取自于训练并能取样 'DDPM'的默认配置。 (基于此篇论文 [\"Denoising Diffusion Probabalistic Models\"](https://arxiv.org/abs/2006.11239):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "ETMq70tpyEtj" }, "outputs": [], "source": [ "from diffusers import DDPMScheduler\n", "\n", "noise_scheduler = DDPMScheduler(num_train_timesteps=1000)" ] }, { "cell_type": "markdown", "metadata": { "id": "4995PVTlyIm1" }, "source": [ "DDPM论文这样来描述一个损坏过程,为每一个 '迭代周期'(timestep)增添一点少量的噪声。设在某个迭代周期有 $x_{t-1}$, 我们可以得到它的下一个版本 $x_t$ (比之前更多一点点噪声):

\n", "\n", "$q(\\mathbf{x}_t \\vert \\mathbf{x}_{t-1}) = \\mathcal{N}(\\mathbf{x}_t; \\sqrt{1 - \\beta_t} \\mathbf{x}_{t-1}, \\beta_t\\mathbf{I}) \\quad\n", "q(\\mathbf{x}_{1:T} \\vert \\mathbf{x}_0) = \\prod^T_{t=1} q(\\mathbf{x}_t \\vert \\mathbf{x}_{t-1})$

\n", "\n", "\n", "这就是说,我们取 $x_{t-1}$, 给他一个$\\sqrt{1 - \\beta_t}$ 的系数,然后加上带有 $\\beta_t$系数的噪声。 这里 $\\beta$ 是根据一些管理器来为每一个t设定的,来决定每一个迭代周期中添加多少噪声。 现在,我们不想把这个推演进行500次来得到 $x_{500}$,所以我们用另一个公式来根据给出的 $x_0$ 计算得到任意t时刻的 $x_t$:

\n", "\n", "$\\begin{aligned}\n", "q(\\mathbf{x}_t \\vert \\mathbf{x}_0) &= \\mathcal{N}(\\mathbf{x}_t; \\sqrt{\\bar{\\alpha}_t} \\mathbf{x}_0, {(1 - \\bar{\\alpha}_t)} \\mathbf{I})\n", "\\end{aligned}$ where $\\bar{\\alpha}_t = \\prod_{i=1}^T \\alpha_i$ and $\\alpha_i = 1-\\beta_i$

\n", "\n", "数学符号看起来总是很可怕!好在有管理器来为我们完成这些运算。我们可以画出 $\\sqrt{\\bar{\\alpha}_t}$ (标记为 `sqrt_alpha_prod`) 和 $\\sqrt{(1 - \\bar{\\alpha}_t)}$ (标记为 `sqrt_one_minus_alpha_prod`) 来看一下输入(x)与噪声是如何在不同迭代周期中量化和叠加的:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "oP-rFQUzdx9h", "outputId": "dea1ec0a-9a08-433a-a8d4-9f8731e2e3ea" }, "outputs": [], "source": [ "plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r\"${\\sqrt{\\bar{\\alpha}_t}}$\")\n", "plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r\"$\\sqrt{(1 - \\bar{\\alpha}_t)}$\")\n", "plt.legend(fontsize=\"x-large\");" ] }, { "cell_type": "markdown", "metadata": { "id": "dRuwInQcyfSQ" }, "source": [ "**练习:** 你可以探索一下使用不同的beta_start时曲线是如何变化的,beta_end 与 beta_schedule可以通过以下注释内容来修改:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "KXDGwKRJyK_s" }, "outputs": [], "source": [ "# One with too little noise added:\n", "# noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.004)\n", "# The 'cosine' schedule, which may be better for small image sizes:\n", "# noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')" ] }, { "cell_type": "markdown", "metadata": { "id": "D0uCdW7UzTvB" }, "source": [ "不论你选择了哪一个管理器(调度器),我们现在都可以使用 `noise_scheduler.add_noise`功能来添加不同程度的噪声,就像这样:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 98 }, "id": "0IPVOilDdzVa", "outputId": "66e73c49-99a7-4004-b3fd-c889a2304339" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Noisy X shape torch.Size([8, 3, 32, 32])\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timesteps = torch.linspace(0, 999, 8).long().to(device)\n", "noise = torch.randn_like(xb)\n", "noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)\n", "print(\"Noisy X shape\", noisy_xb.shape)\n", "show_images(noisy_xb).resize((8 * 64, 64), resample=Image.NEAREST)" ] }, { "cell_type": "markdown", "metadata": { "id": "Q7hsX6nDZiYm" }, "source": [ "再来,在这里探索使用这里不同噪声管理器和预设参数带来的效果。 [This video](https://www.youtube.com/watch?v=fbLgFrlTnGU) 这个视频很好的解释了一些上述数学运算的细节,同时也是对此类概念的一个很好引入介绍。" ] }, { "cell_type": "markdown", "metadata": { "id": "o5RvxO2ss23-" }, "source": [ "## 步骤 4:定义模型" ] }, { "cell_type": "markdown", "metadata": { "id": "gHiMIrLRTk3K" }, "source": [ "现在我们来到了核心部分:模型本身。 \n", "\n", "大多数扩散模型使用的模型结构都是一些[U-net]的变形(https://arxiv.org/abs/1505.04597) 也是我们在这里会用到的结构。\n", "\n", "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/unet-model.png)\n", "\n", "概括来说:\n", "- 输入模型中的图片经过几个由ResNetLayer构成的层,其中每层都使图片尺寸减半。\n", "- 之后在经过同样数量的层把图片升采样。\n", "- 其中还有对特征在相同位置的上、下采样层残差连接模块。\n", "\n", "模型一个关键特征既是,输出图片尺寸与输入图片相同,这正是我们这里需要的。\n", "\n", "Diffusers 为我们提供了一个易用的 `UNet2DModel` 类,用来在PyTorch创建所需要的结构。\n", "\n", "我们来使用U-net为我们生成目标大小的图片吧。\n", "注意这里 `down_block_types` 对应下采样模块 (上图中绿色部分), 而 `up_block_types` 对应上采样模块 (上图中红色部分):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fRGXiotOs4Mc" }, "outputs": [], "source": [ "from diffusers import UNet2DModel\n", "\n", "# Create a model\n", "model = UNet2DModel(\n", " sample_size=image_size, # the target image resolution\n", " in_channels=3, # the number of input channels, 3 for RGB images\n", " out_channels=3, # the number of output channels\n", " layers_per_block=2, # how many ResNet layers to use per UNet block\n", " block_out_channels=(64, 128, 128, 256), # More channels -> more parameters\n", " down_block_types=(\n", " \"DownBlock2D\", # a regular ResNet downsampling block\n", " \"DownBlock2D\",\n", " \"AttnDownBlock2D\", # a ResNet downsampling block with spatial self-attention\n", " \"AttnDownBlock2D\",\n", " ),\n", " up_block_types=(\n", " \"AttnUpBlock2D\",\n", " \"AttnUpBlock2D\", # a ResNet upsampling block with spatial self-attention\n", " \"UpBlock2D\",\n", " \"UpBlock2D\", # a regular ResNet upsampling block\n", " ),\n", ")\n", "model.to(device);" ] }, { "cell_type": "markdown", "metadata": { "id": "y7lkJym26QRo" }, "source": [ "当在处理更高分辨率的输入时,你可能想用更多层的下、上采样模块,让注意力层只聚焦在最低分辨率(最底)层来减少内存消耗。我们在之后会讨论该如何实验来找到最适用与你手头场景的配置方法。" ] }, { "cell_type": "markdown", "metadata": { "id": "483LRBfs0C5_" }, "source": [ "我们可以通过输入一批数据和随机的迭代周期数来看输出是否与输入尺寸相同:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SmmxZJYM0KwA", "outputId": "78bb996d-a28d-4fc4-85cb-42636b9f7a4d" }, "outputs": [ { "data": { "text/plain": [ "torch.Size([8, 3, 32, 32])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with torch.no_grad():\n", " model_prediction = model(noisy_xb, timesteps).sample\n", "model_prediction.shape" ] }, { "cell_type": "markdown", "metadata": { "id": "mIhFUbFMZxj7" }, "source": [ "在下一步中,我们来看如何训练这个模型。" ] }, { "cell_type": "markdown", "metadata": { "id": "vbcdWagYs7tc" }, "source": [ "## 步骤 5:创建训练循环" ] }, { "cell_type": "markdown", "metadata": { "id": "EGQjvwOi64Sl" }, "source": [ "终于可以训练了!下面这是PyTorch中经典的优化迭代循环,在这里一批一批的送入数据然后通过优化器来一步步更新模型参数 - 在这个样例中我们使用学习率为0.0004的AdamW优化器。 \n", "\n", "对于每一批的数据,我们要\n", "- 随机取样几个迭代周期\n", "- 根据预设为数据加入噪声\n", "- 把带噪数据送入模型\n", "- 使用MSE作为损失函数来比较目标结果与模型预测结果(在这里是加入噪声的场景)\n", "- 通过 `loss.backward()` 与 `optimizer.step()`来更新模型参数\n", "\n", "在这个过程中我们记录Loss值用来后续的绘图。\n", "\n", "NB: 这段代码大概需10分钟来运行 - 你也可以跳过以下两块操作直接使用预训练好的模型。供你选择,你可以探索下通过缩小模型层中的通道数会对运行速度有多少提升。" ] }, { "cell_type": "markdown", "metadata": { "id": "Waw8nGdO4S-O" }, "source": [ "官方扩散模型示例 [official diffusers training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) 训练了在更高分辨率数据集上的一个更大的模型,这也是一个极为精简训练循环的优秀示例:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UFRXBcyGs_uO", "outputId": "59f191e5-8f8c-467d-e62c-03274cdc58ea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch:5, loss: 0.16273280512541533\n", "Epoch:10, loss: 0.11161588924005628\n", "Epoch:15, loss: 0.10206522420048714\n", "Epoch:20, loss: 0.08302505919709802\n", "Epoch:25, loss: 0.07805309211835265\n", "Epoch:30, loss: 0.07474562455900013\n" ] } ], "source": [ "# Set the noise scheduler\n", "noise_scheduler = DDPMScheduler(\n", " num_train_timesteps=1000, beta_schedule=\"squaredcos_cap_v2\"\n", ")\n", "\n", "# Training loop\n", "optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)\n", "\n", "losses = []\n", "\n", "for epoch in range(30):\n", " for step, batch in enumerate(train_dataloader):\n", " clean_images = batch[\"images\"].to(device)\n", " # Sample noise to add to the images\n", " noise = torch.randn(clean_images.shape).to(clean_images.device)\n", " bs = clean_images.shape[0]\n", "\n", " # Sample a random timestep for each image\n", " timesteps = torch.randint(\n", " 0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device\n", " ).long()\n", "\n", " # Add noise to the clean images according to the noise magnitude at each timestep\n", " noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n", "\n", " # Get the model prediction\n", " noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n", "\n", " # Calculate the loss\n", " loss = F.mse_loss(noise_pred, noise)\n", " loss.backward(loss)\n", " losses.append(loss.item())\n", "\n", " # Update the model parameters with the optimizer\n", " optimizer.step()\n", " optimizer.zero_grad()\n", "\n", " if (epoch + 1) % 5 == 0:\n", " loss_last_epoch = sum(losses[-len(train_dataloader) :]) / len(train_dataloader)\n", " print(f\"Epoch:{epoch+1}, loss: {loss_last_epoch}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "5g3B7J2y36o-" }, "source": [ "绘制loss曲线,我们能看到模型在一开始快速的收敛,接下来以一个较慢的速度持续优化(我们用右边log坐标轴的视图可以看的更清楚):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 256 }, "id": "DoZP2oHHfL3R", "outputId": "fe16d7a7-8684-4263-b859-fdc6ac254433" }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n", "axs[0].plot(losses)\n", "axs[1].plot(np.log(losses))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "7woNwcna3gkU" }, "source": [ "你可以选择运行上面的代码,也可以这样通过管道来调用模型:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IN21Roo5fVnB" }, "outputs": [], "source": [ "# Uncomment to instead load the model I trained earlier:\n", "# model = butterfly_pipeline.unet" ] }, { "cell_type": "markdown", "metadata": { "id": "qVdGxVJOtBPb" }, "source": [ "## 步骤 6:生成图像\n", "\n", "我们怎么从这个模型中得到图像呢?" ] }, { "cell_type": "markdown", "metadata": { "id": "LTBbByMl7ri3" }, "source": [ "### 方法 1:建立一个管道:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "p3mJYitMtDeB" }, "outputs": [], "source": [ "from diffusers import DDPMPipeline\n", "\n", "image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "8a4c2c2e638d4d1e9dac148d014ab5e5", "0139e5a0e8ab4c0687daa4ea87e5f496", "6c09a1bb320348659689d40ae2cd44ef", "5d443d805c4d405d9ddea69d06e29c64", "e33c2a591703427c9ad7a52098fa5129", "2fe502a96cf94c5d91277ed057e0bbb9", "947c7e5180e647839d8949793fcc6767", "2b89dc53965e43088fe831f5c283d25c", "269bcc0cbf8e43d495cc20396ee85262", "c9fb67b6c9d546059aae7875853191ab", "731f299d38b2482dab9ef543aac76b44" ] }, "id": "Yl_dSb7OgHet", "outputId": "8ba86b5f-4fe5-48bb-c189-42a00bc18dd0" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc2ef0816a264fb4807c35d31f45b8e7", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline_output = image_pipe()\n", "pipeline_output.images[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "UHvu25neFJHh" }, "source": [ "我们可以在本地文件夹这样保存一个管道:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "a8C0_2UVhiWb" }, "outputs": [], "source": [ "image_pipe.save_pretrained(\"my_pipeline\")" ] }, { "cell_type": "markdown", "metadata": { "id": "DHgonGi3FN4J" }, "source": [ "检查文件夹的内容:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "F5hcwUSJhsnv", "outputId": "cacae285-6982-4716-989e-77e8322f06ae" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model_index.json scheduler unet\n" ] } ], "source": [ "!ls my_pipeline/" ] }, { "cell_type": "markdown", "metadata": { "id": "vypV6Joh2vE7" }, "source": [ "这里 `scheduler` 与 `unet` 子文件夹中包含了生成图像所需的全部组件。比如,在 `unet` 文件中能看到模型参数(`diffusion_pytorch_model.bin`) 与描述模型结构的配置文件。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dbFq6QXJFUdD", "outputId": "74064f89-c598-4fe2-beae-a675fde49147" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "config.json diffusion_pytorch_model.bin\n" ] } ], "source": [ "!ls my_pipeline/unet/" ] }, { "cell_type": "markdown", "metadata": { "id": "L2_tiRgS7-qh" }, "source": [ "以上,这些文件包含了重新建立一个管道的全部内容。你可以手动把它们上传到hub来与他人分享你制作的管道,或使用下一节的API方法来记载。" ] }, { "cell_type": "markdown", "metadata": { "id": "wTWtm_4HFi5U" }, "source": [ "### 方法 2:写一个取样循环\n", "如果你去查看了管道中的forward方法,你可以看到在运行 `image_pipe()`时发生了什么:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m-WTv-a5bBOB" }, "outputs": [], "source": [ "# ??image_pipe.forward" ] }, { "cell_type": "markdown", "metadata": { "id": "Bj3tuUD6bMG8" }, "source": [ "从随机噪声开始,遍历管理器的迭代周期来看从最嘈杂直到最微小的噪声变化,基于模型的预测一步步减少一些噪声:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "cAFFaVJ6tFbk", "outputId": "c637da82-1db5-4275-9a98-dfe13f5bfded" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Random starting point (8 random images):\n", "sample = torch.randn(8, 3, 32, 32).to(device)\n", "\n", "for i, t in enumerate(noise_scheduler.timesteps):\n", "\n", " # Get model pred\n", " with torch.no_grad():\n", " residual = model(sample, t).sample\n", "\n", " # Update sample with step\n", " sample = noise_scheduler.step(residual, t, sample).prev_sample\n", "\n", "show_images(sample)" ] }, { "cell_type": "markdown", "metadata": { "id": "70m_LsIVbhAe" }, "source": [ " `noise_scheduler.step()` 函数相应做了 `sample`(取样)时的数学运算。其实有很多取样的方法 - 在下一个单元我们将看到在已有模型的基础上如何换一个不同的取样器来加速图片生成,也会讲到更多从扩散模型中取样的背后原理。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 步骤 7:把你的模型Push到Hub\n", "\n", "在上面的例子中我们把管道保存在了本地。把模型push到hub上,我们会需要建立模型和相应文件的仓库名。我们根据你的选择(模型ID)来决定仓库的名字(大胆的去替换掉 `model_name`吧;需要包含你的用户名,`get_full_repo_name()`会帮你做到):" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'lewtun/sd-class-butterflies-32'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from huggingface_hub import get_full_repo_name\n", "\n", "model_name = \"sd-class-butterflies-32\"\n", "hub_model_id = get_full_repo_name(model_name)\n", "hub_model_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "然后,在 🤗 Hub上创建模型仓库并push它吧:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "xGCNchpSBAcS" }, "outputs": [ { "data": { "text/plain": [ "'https://huggingface.co/lewtun/sd-class-butterflies-32/blob/main/model_index.json'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from huggingface_hub import HfApi, create_repo\n", "\n", "create_repo(hub_model_id)\n", "api = HfApi()\n", "api.upload_folder(\n", " folder_path=\"my_pipeline/scheduler\", path_in_repo=\"\", repo_id=hub_model_id\n", ")\n", "api.upload_folder(folder_path=\"my_pipeline/unet\", path_in_repo=\"\", repo_id=hub_model_id)\n", "api.upload_file(\n", " path_or_fileobj=\"my_pipeline/model_index.json\",\n", " path_in_repo=\"model_index.json\",\n", " repo_id=hub_model_id,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "最后一件事是创建一个超棒的模型卡,如此,我们的蝴蝶生成器可以轻松的在Hub上被找到(请在描述中随意发挥!):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import ModelCard\n", "\n", "content = f\"\"\"\n", "---\n", "license: mit\n", "tags:\n", "- pytorch\n", "- diffusers\n", "- unconditional-image-generation\n", "- diffusion-models-class\n", "---\n", "\n", "# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)\n", "\n", "This model is a diffusion model for unconditional image generation of cute 🦋.\n", "\n", "## Usage\n", "\n", "```python\n", "from diffusers import DDPMPipeline\n", "\n", "pipeline = DDPMPipeline.from_pretrained('{hub_model_id}')\n", "image = pipeline().images[0]\n", "image\n", "```\n", "\"\"\"\n", "\n", "card = ModelCard(content)\n", "card.push_to_hub(hub_model_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "现在模型已经在Hub上了,你可以这样从任何地方使用 `DDPMPipeline`的 `from_pretrained()`方法来下来它:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "01a9f8ae46e74ce58c3174b09dfa6f86", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 4 files: 0%| | 0/4 [00:00" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from diffusers import DDPMPipeline\n", "\n", "image_pipe = DDPMPipeline.from_pretrained(hub_model_id)\n", "pipeline_output = image_pipe()\n", "pipeline_output.images[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "太棒了,成功了!" ] }, { "cell_type": "markdown", "metadata": { "id": "VOWORe9htIBI" }, "source": [ "# 使用🤗 Accelerate来扩大规模 \n", "\n", "这篇笔记是用来教学,为此我尽力保证代码的简洁与轻量化。但也因为这样,我们也略去了一些内容你也许在使用更多数据训练一个更大的模式时,可能所需要用到的内容,如多块GPU支持,进度记录和样例图片,用于支持更大batchsize的导数记录功能,自动上传模型等等。好在这些功能大多数在这个示例代码中包含 [here](https://github.com/huggingface/diffusers/raw/main/examples/unconditional_image_generation/train_unconditional.py).\n", "\n", "你可以这样下载该文件:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "26zRVMa9oq3U" }, "outputs": [], "source": [ "!wget https://github.com/huggingface/diffusers/raw/main/examples/unconditional_image_generation/train_unconditional.py" ] }, { "cell_type": "markdown", "metadata": { "id": "7XMGtxaXc6kQ" }, "source": [ "打开文件,你就可以看到模型是怎么定义的,以及有哪些可选的配置参数。我使用如下命令运行了该代码:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'lewtun/sd-class-butterflies-64'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's give our new model a name for the Hub\n", "model_name = \"sd-class-butterflies-64\"\n", "hub_model_id = get_full_repo_name(model_name)\n", "hub_model_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "isghoKaooniE" }, "outputs": [], "source": [ "!accelerate launch train_unconditional.py \\\n", " --dataset_name=\"huggan/smithsonian_butterflies_subset\" \\\n", " --resolution=64 \\\n", " --output_dir={model_name} \\\n", " --train_batch_size=32 \\\n", " --num_epochs=50 \\\n", " --gradient_accumulation_steps=1 \\\n", " --learning_rate=1e-4 \\\n", " --lr_warmup_steps=500 \\\n", " --mixed_precision=\"no\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如之前一样,把模型push到hub,并且创建一个超酷的模型卡(按你的想法随意填写!):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://huggingface.co/lewtun/sd-class-butterflies-64/blob/main/README.md'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "create_repo(hub_model_id)\n", "api = HfApi()\n", "api.upload_folder(\n", " folder_path=f\"{model_name}/scheduler\", path_in_repo=\"\", repo_id=hub_model_id\n", ")\n", "api.upload_folder(\n", " folder_path=f\"{model_name}/unet\", path_in_repo=\"\", repo_id=hub_model_id\n", ")\n", "api.upload_file(\n", " path_or_fileobj=f\"{model_name}/model_index.json\",\n", " path_in_repo=\"model_index.json\",\n", " repo_id=hub_model_id,\n", ")\n", "\n", "content = f\"\"\"\n", "---\n", "license: mit\n", "tags:\n", "- pytorch\n", "- diffusers\n", "- unconditional-image-generation\n", "- diffusion-models-class\n", "---\n", "\n", "# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)\n", "\n", "This model is a diffusion model for unconditional image generation of cute 🦋.\n", "\n", "## Usage\n", "\n", "```python\n", "from diffusers import DDPMPipeline\n", "\n", "pipeline = DDPMPipeline.from_pretrained('{hub_model_id}')\n", "image = pipeline().images[0]\n", "image\n", "```\n", "\"\"\"\n", "\n", "card = ModelCard(content)\n", "card.push_to_hub(hub_model_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "StJOioIqdOOQ" }, "source": [ "大概45分钟之后,得到这样的结果:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "60b5a9bd856342d9b9e656c7962551c0", "845d19fc07fb4db198710258f8cd82fc", "cf19bc72f2544217bba52542a5bbd5d8", "072322f017d046ffb2c161b0b091bc03", "42ab43b3645641e7bddb7948f2ae48da", "d9cc307233a74d5bb2f0fcc6467c26fb", "34c9e90ecde6463dabbc802c510f2e7c", "1f1d0195b44d43e79f3cce0fbf5ff575", "d0a1c578c07747188f022421767b1c02", "6907ff28bad14fcdbc7bbc7ccd6df4cd", "055eea9718324654bc1c9fc63fc7c171" ] }, "id": "pxI6aqVnHe10", "outputId": "6453d1de-ad1c-4114-b648-cdc8526ddb06" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "07d1a62245d748f4b0249c613b06663a", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline = DDPMPipeline.from_pretrained(hub_model_id).to(device)\n", "images = pipeline(batch_size=8).images\n", "make_grid(images)" ] }, { "cell_type": "markdown", "metadata": { "id": "y5EtNv12dTy_" }, "source": [ "**练习:** 看看你能不能找到训练出在短时内能得到满意结果的模型训练设置参数,并与社群分享你的发现。阅读这些脚本看看你能不能理解它们,如果遇到了一些看上去令人迷惑的地方,请向大家提问来寻求解释。" ] }, { "cell_type": "markdown", "metadata": { "id": "pEa6TgCFtQwv" }, "source": [ "# 更高阶的探索之路\n", "\n", "希望这些能够让你初步了解可以使用 🤗 Diffusers library来做什么!可能一些后续的步骤是这样:\n", "\n", "- 尝试在新数据集上训练一个无限制的扩散模型 - 如果你能直接自己完成那就太好了 [create one yourself](https://huggingface.co/docs/datasets/image_dataset). 你可以在Hub这里找到一些能完成这个任务的超棒图像数据集 [HugGan organization](https://huggingface.co/huggan). 如果你不想等待模型训练太久的话,一定记得对图片做下采样!\n", "- 试试用DreamBooth来创建你自己定制的扩散模型管道,看看这里 [this Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) 或者 [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)\n", "- 修改训练脚本来探索使用不同的UNet超参数(层数深度,通道数等等),不同的噪声管理器等等。\n", "- 来瞧瞧 [Diffusion Models from Scratch](https://github.com/huggingface/diffusion-models-class/blob/main/unit1/02_diffusion_models_from_scratch.ipynb) 在本单元的核心思想之上的一些不同看法。\n", "\n", "祝好,敬请关注第2单元!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6 (default, Sep 26 2022, 11:37:49) \n[Clang 14.0.0 (clang-1400.0.29.202)]" }, "vscode": { "interpreter": { 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