notebooks/01_migrate_stable_diffusion_quickstart.ipynb · ModelArts/ModelArts-Ascend

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01_migrate_stable_diffusion_quickstart.ipynb 550.62 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d701cad-28aa-401a-9e70-2dc1800543bd",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Stable Diffusion模型迁移到Ascend上进行推理\n",
    "本文以stable-diffusion模型为例，示例如何将stable diffusion模型通过MSLite进行转换后, 迁移在昇腾设备上运行。原理是将SD的onnx模型通过mindspore lite convertor转换为MindIR格式，进而可以通过mindsporelite运行时引擎直接运行到昇腾设备上。\n",
    "\n",
    "Stable Diffusion代码仓地址为：https://github.com/huggingface/diffusers。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c2475b-2dd6-493c-b53f-d73b750de98a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 步骤1：环境准备"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6ce913-f89c-4bad-afaf-498fe959d697",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**1.1 安装必要的第三方依赖, 包含torch onnx tranformer等**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dfecbdb-dcca-45a0-99b8-e030b27bb746",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple\n",
      "Collecting torch==1.13.0\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/4f/78/0e87938c508f8b50b429c34234932fb1556c62a2f0a3774ee78652aef707/torch-1.13.0-cp37-cp37m-manylinux2014_aarch64.whl (73.3 MB)\n",
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      "\u001b[?25hCollecting onnx\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/18/c7/d457101bc9fbde788c08c9ad4429589add063521df5f25b25555864b5cfb/onnx-1.14.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (15.0 MB)\n",
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      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/87/f0/2a152ed10ab8601431e87a606d397f7473c5fa4f8162f4ec5bda6ddb2df4/transformers-4.27.4-py3-none-any.whl (6.8 MB)\n",
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      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/0a/ca/96a50d122bd07d06c66e20e6bd275b5c8829602398f4e141f8755a25e31e/accelerate-0.17.1-py3-none-any.whl (212 kB)\n",
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      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/2d/16/88a3851e1b0c1ecc839f565f21a26ef1bdc4543e3b1052deda722de0e005/onnxruntime-1.14.1-cp37-cp37m-manylinux_2_27_aarch64.whl (4.7 MB)\n",
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      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/df/90/5ad98abead047169f4f86bc67e99020c841d71c9c6bd202e04af71e70e53/huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
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      "Collecting protobuf>=3.20.2\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/b4/39/be34246e573270e3db7bc74690ddfdaf5d5b267129d657d278949a496572/protobuf-4.23.2-cp37-abi3-manylinux2014_aarch64.whl (303 kB)\n",
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      "\u001b[?25hCollecting flatbuffers\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/6f/12/d5c79ee252793ffe845d58a913197bfa02ae9a0b5c9bc3dc4b58d477b9e7/flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)\n",
      "Collecting coloredlogs\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
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      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/b7/0d/86662f93102e42545cdf031da4fddf0ace9030ec67478932a628afc5973b/numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB)\n",
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      "Collecting humanfriendly>=9.1\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
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      "Requirement already satisfied: mpmath>=0.19 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages (from sympy->onnxruntime) (1.2.1)\n",
      "Installing collected packages: humanfriendly, filelock, torch, tokenizers, regex, protobuf, numpy, huggingface-hub, flatbuffers, coloredlogs, transformers, onnxruntime, onnx, accelerate\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 3.20.1\n",
      "    Uninstalling protobuf-3.20.1:\n",
      "      Successfully uninstalled protobuf-3.20.1\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.21.2\n",
      "    Uninstalling numpy-1.21.2:\n",
      "      Successfully uninstalled numpy-1.21.2\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "te 0.4.0 requires synr==0.5.0, which is not installed.\n",
      "mindinsight 1.9.0 requires Flask>=2.1.0, but you have flask 2.0.1 which is incompatible.\n",
      "mindinsight 1.9.0 requires protobuf<=3.20.1,>=3.13.0, but you have protobuf 4.23.2 which is incompatible.\n",
      "modelarts 1.4.18 requires attrs==21.4.0, but you have attrs 19.3.0 which is incompatible.\n",
      "modelarts 1.4.18 requires matplotlib==3.5.2, but you have matplotlib 3.5.1 which is incompatible.\n",
      "modelarts 1.4.18 requires Pillow<=9.3.0, but you have pillow 9.4.0 which is incompatible.\n",
      "modelarts 1.4.18 requires typing-extensions<=4.0.1, but you have typing-extensions 4.5.0 which is incompatible.\n",
      "modelarts 1.4.18 requires urllib3<=1.26.13, but you have urllib3 1.26.14 which is incompatible.\n",
      "ma-cau 1.1.6 requires click==8.0.3, but you have click 8.1.3 which is incompatible.\n",
      "ma-cau 1.1.6 requires huaweicloudsdkcore==3.0.50, but you have huaweicloudsdkcore 3.1.8 which is incompatible.\n",
      "ma-cau 1.1.6 requires Pillow==9.1.1, but you have pillow 9.4.0 which is incompatible.\n",
      "ma-cau 1.1.6 requires pycocotools==2.0.3, but you have pycocotools 2.0.0 which is incompatible.\n",
      "ma-cau 1.1.6 requires tenacity<=8.0.1, but you have tenacity 8.1.0 which is incompatible.\n",
      "ma-cau 1.1.6 requires terminaltables~=3.1.10, but you have terminaltables 3.1.0 which is incompatible.\n",
      "ma-cau 1.1.6 requires typing-extensions<=4.0.1, but you have typing-extensions 4.5.0 which is incompatible.\u001b[0m\n",
      "Successfully installed accelerate-0.17.1 coloredlogs-15.0.1 filelock-3.11.0 flatbuffers-23.5.26 huggingface-hub-0.13.4 humanfriendly-10.0 numpy-1.21.6 onnx-1.14.0 onnxruntime-1.14.1 protobuf-4.23.2 regex-2023.5.5 tokenizers-0.13.2 torch-1.13.0 transformers-4.27.4\n",
      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 23.0.1 is available.\n",
      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install torch==1.13.0 onnx transformers==4.27.4 accelerate onnxruntime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75a2deb-7148-4209-81b5-62d94b1ce3ce",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**1.2 从Github上Clone stable diffusion源代码**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be0601f-6b93-4342-92c6-df9b09f07fc5",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "源码中包含了推理应用的pipeline代码（stable diffusion模型包含多个模型，且多个模型的级联关系和前后处理相对比较复杂），可以复用大部分官方的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33795af8-69a1-448b-b7ea-c83057afd4b5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'diffusers'...\n",
      "remote: Enumerating objects: 38354, done.\u001b[K\n",
      "remote: Counting objects: 100% (185/185), done.\u001b[K\n",
      "remote: Compressing objects: 100% (106/106), done.\u001b[K\n",
      "remote: Total 38354 (delta 86), reused 132 (delta 65), pack-reused 38169\u001b[K\n",
      "Receiving objects: 100% (38354/38354), 24.28 MiB | 2.16 MiB/s, done.\n",
      "Resolving deltas: 100% (28338/28338), done.\n",
      "Note: checking out 'f7bb9cae114575b5ce2b94c122332114a0def106'.\n",
      "\n",
      "You are in 'detached HEAD' state. You can look around, make experimental\n",
      "changes and commit them, and you can discard any commits you make in this\n",
      "state without impacting any branches by performing another checkout.\n",
      "\n",
      "If you want to create a new branch to retain commits you create, you may\n",
      "do so (now or later) by using -b with the checkout command again. Example:\n",
      "\n",
      "  git checkout -b <new-branch-name>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/huggingface/diffusers.git -b v0.11.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d550668-b7a0-4386-ad3c-cbff24e54b18",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**1.3 下载待迁移的Stable Diffusion模型**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583f4056-8b03-4d6d-a1fe-3c4d71bc1715",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "官方的stable diffusion模型在huggingface上公开了PyTorch的模型权重以及已经转换好的onnx模型，由于权重文件较大，可以选择从OBS中下载。\n",
    "其中`pytorch_model`表示原始的PyTorch模型权重，`torch_to_onnx`表示onnx格式的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "794a509f-0493-464d-895f-3e00e2d7dfc9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-09 11:35:57-- \n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 15437570218 (14G) [application/zip]\n",
      "Saving to: ‘pytorch_model.zip’\n",
      "\n",
      "pytorch_model.zip   100%[===================>]  14.38G   213MB/s    in 66s     \n",
      "\n",
      "2023-06-09 11:37:03 (224 MB/s) - ‘pytorch_model.zip’ saved [15437570218/15437570218]\n",
      "\n",
      "Archive:  pytorch_model.zip\n",
      "   creating: pytorch_model/\n",
      "   creating: pytorch_model/feature_extractor/\n",
      "  inflating: pytorch_model/feature_extractor/preprocessor_config.json  \n",
      "  inflating: pytorch_model/model_index.json  \n",
      "   creating: pytorch_model/scheduler/\n",
      "  inflating: pytorch_model/scheduler/scheduler_config.json  \n",
      "   creating: pytorch_model/unet/\n",
      "  inflating: pytorch_model/unet/config.json  \n",
      "  inflating: pytorch_model/unet/diffusion_pytorch_model.bin  \n",
      "  inflating: pytorch_model/v1-5-pruned.ckpt  \n",
      "   creating: pytorch_model/vae/\n",
      "  inflating: pytorch_model/vae/config.json  \n",
      "  inflating: pytorch_model/vae/diffusion_pytorch_model.bin  \n",
      "   creating: pytorch_model/tokenizer/\n",
      "  inflating: pytorch_model/tokenizer/special_tokens_map.json  \n",
      "  inflating: pytorch_model/tokenizer/vocab.json  \n",
      "  inflating: pytorch_model/tokenizer/tokenizer_config.json  \n",
      "  inflating: pytorch_model/tokenizer/merges.txt  \n",
      "   creating: pytorch_model/text_encoder/\n",
      "  inflating: pytorch_model/text_encoder/config.json  \n",
      "  inflating: pytorch_model/text_encoder/pytorch_model.bin  \n",
      "   creating: pytorch_model/safety_checker/\n",
      "  inflating: pytorch_model/safety_checker/config.json  \n",
      "  inflating: pytorch_model/safety_checker/pytorch_model.bin  \n",
      "  inflating: pytorch_model/v1-5-pruned-emaonly.ckpt  \n"
     ]
    }
   ],
   "source": [
    "# 下载PyTorch模型权重，用于分析模型的输入shape\n",
    "! wget https://cnnorth4-modelarts-sdk.obs.cn-north-4.myhuaweicloud.com/modelarts/advisor/sample/pytorch_model.zip && \\\n",
    "unzip pytorch_model.zip && \\\n",
    "rm -rf pytorch_model.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fb895f6-eab4-47a8-88d1-4b77fabae463",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-09 11:45:17--  torch_to_onnx.zip\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4561223703 (4.2G) [application/zip]\n",
      "Saving to: ‘torch_to_onnx.zip’\n",
      "\n",
      "torch_to_onnx.zip   100%[===================>]   4.25G   130MB/s    in 34s     \n",
      "\n",
      "2023-06-09 11:45:52 (128 MB/s) - ‘torch_to_onnx.zip’ saved [4561223703/4561223703]\n",
      "\n",
      "Archive:  torch_to_onnx.zip\n",
      "   creating: torch_to_onnx/\n",
      "   creating: torch_to_onnx/feature_extractor/\n",
      "  inflating: torch_to_onnx/feature_extractor/preprocessor_config.json  \n",
      "  inflating: torch_to_onnx/model_index.json  \n",
      "   creating: torch_to_onnx/scheduler/\n",
      "  inflating: torch_to_onnx/scheduler/scheduler_config.json  \n",
      "   creating: torch_to_onnx/unet/\n",
      "  inflating: torch_to_onnx/unet/weights.pb  \n",
      "  inflating: torch_to_onnx/unet/model.onnx  \n",
      "   creating: torch_to_onnx/tokenizer/\n",
      "  inflating: torch_to_onnx/tokenizer/special_tokens_map.json  \n",
      "  inflating: torch_to_onnx/tokenizer/vocab.json  \n",
      "  inflating: torch_to_onnx/tokenizer/tokenizer_config.json  \n",
      "  inflating: torch_to_onnx/tokenizer/merges.txt  \n",
      "   creating: torch_to_onnx/text_encoder/\n",
      "  inflating: torch_to_onnx/text_encoder/model.onnx  \n",
      "   creating: torch_to_onnx/vae_encoder/\n",
      "  inflating: torch_to_onnx/vae_encoder/model.onnx  \n",
      "   creating: torch_to_onnx/safety_checker/\n",
      "  inflating: torch_to_onnx/safety_checker/model.onnx  \n",
      "   creating: torch_to_onnx/vae_decoder/\n",
      "  inflating: torch_to_onnx/vae_decoder/model.onnx  \n"
     ]
    }
   ],
   "source": [
    "# 下载onnx模型文件，用于模型转换\n",
    "! wget https://cnnorth4-modelarts-sdk.obs.cn-north-4.myhuaweicloud.com/modelarts/advisor/sample/torch_to_onnx.zip && \\\n",
    "unzip torch_to_onnx.zip && \\\n",
    "rm -rf torch_to_onnx.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ca3100-9116-4392-9593-9ae91bb83758",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Stable Diffusion img2img图像生成pipeline由五个模型组成，分别为：\n",
    "\n",
    "* text_encoder: 文本编码器，负责将文本prompt嵌入到latent空间；\n",
    "* vae_encoder: 图像编码器，负责将蒙版图片嵌入到latent空间的隐含向量;\n",
    "* unet + scheduler: 多模态扩散模型及调度程序，在文本引导下进行多轮预测，在latent空间中将随机高斯噪声转化成图片隐含向量；\n",
    "* vae_decoder： 图像解码器，负责将latent空间的图片隐含向量解码为图像；\n",
    "* safety_checker: 安全检查器，主要用于审核过滤违规图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a089d63-c2d0-4e87-856a-f702b273787c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 72K\n",
      "drwxr----- 12 ma-user ma-group 4.0K Sep  9 10:52 .\n",
      "drwxr-x---  7 ma-user ma-group 4.0K Sep  9 10:51 ..\n",
      "-rwxr-----  1 ma-user ma-group 1.5K Sep  9 10:42 .gitattributes\n",
      "drwxr-x---  2 ma-user ma-group 4.0K Sep  9 10:42 .ipynb_checkpoints\n",
      "-rwxr-----  1 ma-user ma-group  14K Sep  9 10:42 README.md\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 feature_extractor\n",
      "-rwxr-----  1 ma-user ma-group  599 Sep  9 10:42 model_index.json\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 safety_checker\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 scheduler\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 text_encoder\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 tokenizer\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 unet\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 unet_bak\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 vae_decoder\n",
      "drwxr-----  2 ma-user ma-group 4.0K Sep  9 10:42 vae_encoder\n"
     ]
    }
   ],
   "source": [
    "!ls -alh torch_to_onnx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87fc2284-a9bc-424f-bdfb-ff4297953555",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Step2: 模型转换，将onnx模型转mindir模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf594ca-6ea9-4f6b-95ea-446ee8d67b20",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "MindSpore Lite支持昇腾的后端，所以我们可以将对应的模型转换为MindIR格式，进而直接运行到昇腾设备上。\n",
    "\n",
    "具体操作有如下内容：\n",
    "1. 配置必要的环境变量启用MSLite converter_lite\n",
    "2. 对于onnx模型进行MindIR格式转换\n",
    "3. 转换时的--inputShape参数可以通过netron工具进行查看，注意由于我们是进行昇腾的迁移, 注意需要指定\"--device=Ascend\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cb1e13-8bdf-4d9c-98d6-f9f75cec1287",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**2.1 获取模型的输入shape信息**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967bbc5b-2ac1-4759-8b5e-9dae92efb7aa",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "  在进行mindir模型转换时，特别是转换为Ascend模型时，建议指定--inputShape参数，即模型的输入节点名称和输入节点的尺寸大小，格式为`节点名称:节点尺寸`，比如`input:1,3,224,224`表示输入节点名称为`input`，对应的输入尺寸为`(1, 3, 224, 224)`。如果模型有多个输入，可以使用`;`进行分隔，比如`input1:1,3,224,224;input2:1,20,30`。\n",
    "\n",
    "  一般来说，输入节点的名称和尺寸可以通过netron可视化onnx文件进行查看，但是本例导出的Stable Diffusion模型比较特殊，官方导出的onnx模型都是动态维度，无法从onnx文件中得到有效的尺寸信息（只能得到输入节点的名称）。\n",
    "\n",
    "  此时一般需要通过onnx转换脚本或者模型训练或者推理脚本来确定输入的尺寸。在`diffusers`的`convert_stable_diffusion_checkpoint_to_onnx.py`脚本中，导出onnx模型时也需要指定输入尺寸信息，我们可以参考一下每个模型的名称和shape信息。下面的脚本省略了onnx导出过程，只展示如何获取对应的shape信息。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a58bebf6-e2ae-406f-9339-6fd9afb9c34f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing get_sd_infer_shape.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile get_sd_infer_shape.py\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "from diffusers import StableDiffusionPipeline\n",
    "\n",
    "pipeline = StableDiffusionPipeline.from_pretrained(\"pytorch_model\", torch_dtype=torch.float32)\n",
    "\n",
    "# TEXT ENCODER\n",
    "num_tokens = pipeline.text_encoder.config.max_position_embeddings\n",
    "text_hidden_size = pipeline.text_encoder.config.hidden_size\n",
    "text_input = pipeline.tokenizer(\n",
    "    \"A sample prompt\",\n",
    "    padding=\"max_length\",\n",
    "    max_length=pipeline.tokenizer.model_max_length,\n",
    "    truncation=True,\n",
    "    return_tensors=\"pt\",\n",
    ")\n",
    "print(\"# TEXT ENCODER\")\n",
    "print(f\"input_ids: {np.array(text_input.input_ids.shape).tolist()}\")\n",
    "\n",
    "# UNET\n",
    "unet_in_channels = pipeline.unet.config.in_channels\n",
    "unet_sample_size = pipeline.unet.config.sample_size\n",
    "print(\"# UNET\")\n",
    "print(f\"sample: [{2}, {unet_in_channels} {unet_sample_size} {unet_sample_size}]\")\n",
    "print(f\"timestep: [{1}]\")  # 此处应该是1，否则和后续的推理脚本不一致\n",
    "print(f\"encoder_hidden_states: [{2}, {num_tokens} {text_hidden_size}]\")\n",
    "\n",
    "# VAE ENCODER\n",
    "vae_encoder = pipeline.vae\n",
    "vae_in_channels = vae_encoder.config.in_channels\n",
    "vae_sample_size = vae_encoder.config.sample_size\n",
    "print(\"# VAE ENCODER\")\n",
    "print(f\"sample: [{1}, {vae_in_channels}, {vae_sample_size}, {vae_sample_size}]\")\n",
    "\n",
    "# VAE DECODER\n",
    "vae_decoder = pipeline.vae\n",
    "vae_latent_channels = vae_decoder.config.latent_channels\n",
    "vae_out_channels = vae_decoder.config.out_channels\n",
    "print(\"# VAE DECODER\")\n",
    "print(f\"latent_sample: [{1}, {vae_latent_channels}, {unet_sample_size}, {unet_sample_size}]\")\n",
    "\n",
    "# SAFETY CHECKER\n",
    "safety_checker = pipeline.safety_checker\n",
    "clip_num_channels = safety_checker.config.vision_config.num_channels\n",
    "clip_image_size = safety_checker.config.vision_config.image_size\n",
    "print(\"# SAFETY CHECKER\")\n",
    "print(f\"clip_input: [{1}, {clip_num_channels}, {clip_image_size}, {clip_image_size}]\")\n",
    "print(f\"images: [{1}, {vae_sample_size}, {vae_sample_size}, {vae_out_channels}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419f3e8a-65ad-4ad3-b8ec-5cf6291fd1ce",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "根据pipeline的配置信息可以得到每个模型的输入shape信息，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed921c0c-2191-4c30-9cbc-72e4366f960c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# TEXT ENCODER\n",
      "input_ids: [1, 77]\n",
      "# UNET\n",
      "sample: [2, 4 64 64]\n",
      "timestep: [1]\n",
      "encoder_hidden_states: [2, 77 768]\n",
      "# VAE ENCODER\n",
      "latent_sample: [1, 3, 512, 512]\n",
      "# VAE DECODER\n",
      "sample: [1, 4, 64, 64]\n",
      "# SAFETY CHECKER\n",
      "clip_input: [1, 3, 224, 224]\n",
      "images: [1, 512, 512, 3]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n",
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/transformers/models/clip/feature_extraction_clip.py:31: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "export PYTHONPATH=$(pwd)/diffusers/src:$PYTHONPATH \n",
    "python get_sd_infer_shape.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb106bc-9a20-4a3d-bf6c-404bcc9cd79f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**2.2 使用converter_lite转换onnx模型为mindir**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25e570c-fe55-4338-b0d0-4ec7d0816e51",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "模型转换工具converter_lite支持将onnx模型转换为mindir模型，转换时需要指定模型的inputShape信息，inputShape的格式为`\"node_name1:2,1024;node_name2:2,36,1024\"`，表示有两个输入，输入名称分别为`node_name1`和`node_name2`，对应的shape为`2,1024`和`2,36,1024`，多个输入之间用`;`隔开。\n",
    "\n",
    "转换时间较长（10min左右）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61bc13d0-4feb-4b61-a037-8485c3137e46",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing convert_onnx_to_mindir.sh\n"
     ]
    }
   ],
   "source": [
    "%%writefile convert_onnx_to_mindir.sh\n",
    " \n",
    "echo \"########################### begin converter_lite ###########################\"\n",
    " \n",
    "ROOT_PATH=./torch_to_onnx\n",
    "MINIR_PATH=./mindir\n",
    "mkdir -p $MINIR_PATH\n",
    "common=\" --fmk=ONNX --saveType=MINDIR --device=Ascend\"\n",
    " \n",
    "echo -e \"\\n\\n########################### text_encoder ###########################\"\n",
    "model_path=${ROOT_PATH}/text_encoder\n",
    "converter_lite --modelFile=${model_path}/model.onnx --outputFile=${MINIR_PATH}/text_encoder --inputShape=input_ids:1,77 $common\n",
    " \n",
    "echo -e \"\\n\\n########################### unet ###########################\"\n",
    "model_path=${ROOT_PATH}/unet\n",
    "converter_lite --modelFile=${model_path}/model.onnx --outputFile=${MINIR_PATH}/unet --inputShape=\"sample:2,4,64,64;timestep:1;encoder_hidden_states:2,77,768\" $common\n",
    " \n",
    " \n",
    "echo -e \"\\n\\n########################### vae_encoder ###########################\"\n",
    "model_path=${ROOT_PATH}/vae_encoder\n",
    "converter_lite --modelFile=${model_path}/model.onnx --outputFile=${MINIR_PATH}/vae_encoder --inputShape=sample:1,3,512,512 $common\n",
    " \n",
    "echo -e \"\\n\\n########################### vae_decoder ###########################\"\n",
    "model_path=${ROOT_PATH}/vae_decoder\n",
    "converter_lite --modelFile=${model_path}/model.onnx --outputFile=${MINIR_PATH}/vae_decoder --inputShape=latent_sample:1,4,64,64 $common\n",
    " \n",
    "echo -e \"\\n\\n########################### safety_checker ###########################\"\n",
    "model_path=${ROOT_PATH}/safety_checker\n",
    "converter_lite --modelFile=${model_path}/model.onnx --outputFile=${MINIR_PATH}/safety_checker --inputShape=\"clip_input:1,3,224,224;images:1,512,512,3\" $common\n",
    " \n",
    "echo \"########################### end converter_lite ###########################\\n\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "038dcc0b-0c3f-4bf2-8bc5-d3af7a5afbc6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################### begin converter_lite ###########################\n",
      "\n",
      "\n",
      "########################### text_encoder ###########################\n",
      "CONVERT RESULT SUCCESS:0\n",
      "\n",
      "\n",
      "########################### unet ###########################\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:00.432.205 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Resize_3561\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:00.432.934 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Resize_4928\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:00.433.637 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Resize_6295\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:12.567.629 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:12.827.890 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:13.117.310 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:13.486.889 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:13.497.317 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1919542,ffffae6a5010,converter_lite):2023-09-09-11:02:13.507.677 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      ".....CONVERT RESULT SUCCESS:0\n",
      "\n",
      "\n",
      "########################### vae_encoder ###########################\n",
      "[WARNING] LITE(1928704,ffffb4b29010,converter_lite):2023-09-09-11:06:04.849.558 [mindspore/lite/tools/optimizer/common/gllo_utils.cc:217] CopyDataFromInt64] int64 data -9223372036854775807 cannot fit into int32\n",
      "[WARNING] LITE(1928704,ffffb4b29010,converter_lite):2023-09-09-11:06:04.849.890 [mindspore/lite/tools/optimizer/common/gllo_utils.cc:217] CopyDataFromInt64] int64 data -9223372036854775807 cannot fit into int32\n",
      "[WARNING] LITE(1928704,ffffb4b29010,converter_lite):2023-09-09-11:06:04.850.169 [mindspore/lite/tools/optimizer/common/gllo_utils.cc:217] CopyDataFromInt64] int64 data -9223372036854775807 cannot fit into int32\n",
      ".[11:06:45] buffer_reuse_finder.cc:49: Warning: one of pingpong var is null! ping[16] = 0xaaaae69dadd0, pong[38] = 0\n",
      "[11:06:45] buffer_reuse_finder.cc:49: Warning: one of pingpong var is null! ping[15] = 0, pong[37] = 0\n",
      "[11:06:45] buffer_reuse_finder.cc:49: Warning: one of pingpong var is null! ping[5] = 0, pong[27] = 0\n",
      "[11:06:45] buffer_reuse_finder.cc:49: Warning: one of pingpong var is null! ping[6] = 0xaaaae69c2a80, pong[28] = 0\n",
      "..CONVERT RESULT SUCCESS:0\n",
      "\n",
      "\n",
      "########################### vae_decoder ###########################\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:03.356.517 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Resize_257\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:03.356.608 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Resize_340\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:03.356.657 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Resize_424\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:04.028.893 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:04.033.264 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:04.037.866 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:04.047.981 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:04.049.889 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      "[WARNING] LITE(1932300,ffff82689010,converter_lite):2023-09-09-11:07:04.051.823 [mindspore/lite/tools/optimizer/graph/lite_tensor_extractor.cc:189] GetCNodeConstInputToAbstract] abstract already have const data.\n",
      ".CONVERT RESULT SUCCESS:0\n",
      "\n",
      "\n",
      "########################### safety_checker ###########################\n",
      "[WARNING] LITE(1934884,ffff820c3010,converter_lite):2023-09-09-11:07:59.649.955 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Clip_2478\n",
      "[WARNING] LITE(1934884,ffff820c3010,converter_lite):2023-09-09-11:07:59.650.034 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Clip_2482\n",
      "[WARNING] LITE(1934884,ffff820c3010,converter_lite):2023-09-09-11:07:59.650.057 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Clip_2487\n",
      "[WARNING] LITE(1934884,ffff820c3010,converter_lite):2023-09-09-11:07:59.650.074 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3653] CheckOnnxModel] Can not find node input:  of Clip_2491\n",
      "...CONVERT RESULT SUCCESS:0\n",
      "########################### end converter_lite ###########################\\n\n"
     ]
    }
   ],
   "source": [
    "!bash convert_onnx_to_mindir.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb039d0-73a4-4592-852b-81598c0d192d",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "转换成功后将会在`./mindir`文件夹下得到对应的`.mindir`模型，其中同名的`.om`模型是为了备份和调试使用，可以忽略。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bbaf613-c667-4db0-8c33-aa3cab0ab8a2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 3708524\n",
      "-r-------- 1 ma-user ma-group  616639260 Sep  9 11:09 safety_checker.mindir\n",
      "-rw------- 1 ma-user ma-group  616637876 Sep  9 11:09 safety_checker.om\n",
      "-r-------- 1 ma-user ma-group  247922539 Sep  9 11:01 text_encoder.mindir\n",
      "-rw------- 1 ma-user ma-group  247921111 Sep  9 11:01 text_encoder.om\n",
      "-rw------- 1 ma-user ma-group 1726975972 Sep  9 11:05 unet.om\n",
      "-r-------- 1 ma-user ma-group        833 Sep  9 11:06 unet_graph.mindir\n",
      "drwx------ 2 ma-user ma-group       4096 Sep  9 11:06 unet_variables\n",
      "-r-------- 1 ma-user ma-group  100771657 Sep  9 11:07 vae_decoder.mindir\n",
      "-rw------- 1 ma-user ma-group  100770933 Sep  9 11:07 vae_decoder.om\n",
      "-r-------- 1 ma-user ma-group   69927171 Sep  9 11:07 vae_encoder.mindir\n",
      "-rw------- 1 ma-user ma-group   69926458 Sep  9 11:07 vae_encoder.om\n"
     ]
    }
   ],
   "source": [
    "!ls -l ./mindir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb2f1f3-3031-44d7-b0de-909cc4513698",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### 注意：\n",
    "使用mindspore 转换工具converter_lite，将onnx模型转换为mindir模型\n",
    "\n",
    "converter_lite使用指导：[MindSpore-converter_lite](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html)\n",
    "\n",
    "基础Ascend模型转换命令：\n",
    "```shell\n",
    "converter_lite --modelFile=${model_path} --outputFile=${output_model_path} --inputShape=\"input_port_name1:n,c,h,w;input_port_name2:n,c,h,w\" --fmk=ONNX   --saveType=MINDIR --device=Ascend\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e4695d-00a9-415a-bd0a-c082e0efc457",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 步骤3：推理应用使用\n",
    "mindspore_lite目前提供了`python`、`C++`以及`JAVA`三种应用开发接口，这里我们以`python`接口为例，介绍如何使用mindspore_lite API构建并推理Stable Diffusion模型。[官方文档链接](https://www.mindspore.cn/lite/docs/zh-CN/master/use/cloud_infer/runtime_python.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6f8ed2-77a2-4839-9342-6dce07da43c7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**3.1 基于mindspore_lite编写推理脚本**\n",
    "\n",
    "在Ascend上使用mindspore_lite进行推理时一般需要先设置目标设备上下文，然后构建推理模型，获取输入数据，模型预测并得到最终的结果。一个基础的推理框架写法如下所示：\n",
    "\n",
    "```python\n",
    "import mindspore_lite as mslite\n",
    " \n",
    "# 设置目标设备上下文为Ascend，指定device_id为0\n",
    "context = mslite.Context()\n",
    "context.target = [\"ascend\"]\n",
    "context.ascend.device_id = 0\n",
    " \n",
    " \n",
    "# 构建模型\n",
    "model = mslite.Model()\n",
    "model.build_from_file(\"./model.mindir\", mslite.ModelType.MINDIR, context)\n",
    " \n",
    "# 输入数据到Device侧，针对于多输入场景可以通过list来指定输入\n",
    "in_data = [np.array(data1), np.array(data2)]\n",
    "inputs = model.get_inputs()\n",
    "for i, _inputs in enumerate(inputs):\n",
    "    _input.set_data_from_numpy(in_data[i])\n",
    " \n",
    "# 前向推理，并将结果从device侧传到host侧\n",
    "outputs = model.predict(inputs)\n",
    "outputs = [output.get_data_to_numpy() for output in outputs]\n",
    " \n",
    "# 后处理...\n",
    "```\n",
    "\n",
    "本案例中，Stable Diffusion是一个多阶段的模型，一共由text_encoder、UNET、VAE ENCODER、VAE DECODER和SAFETY CHECKER五个模型组成，因此提供了通用的模型代理类,通过代理类对mindir模型进行初始化，然后将推理代码中运行的onnx模型替换为mindir模型即可。\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "import mindspore_lite as mslite\n",
    "import os\n",
    "\n",
    "# mindir模型代理类\n",
    "class MsliteModel:\n",
    "    def __init__(self, model_path, model_name='ms model', device_type='ascend', use_ascend=True,\n",
    "                 onnx_runtime_model=None, get_shape=False) -> None:\n",
    "        \"\"\"\n",
    "        mindir模型代理类\n",
    "        Args:\n",
    "            model_path: mindir文件路径\n",
    "            model_name: 模型名称\n",
    "            device_type: 设备类型\n",
    "            use_ascend: 是否使用Ascend\n",
    "            onnx_runtime_model: onnx模型对象\n",
    "            get_shape: 是否获取输出参数shape\n",
    "        \"\"\"\n",
    "        print('model_path:{}'.format(model_path))\n",
    "        self.model_name = model_name\n",
    "        self.context = MsliteModel.init_context(device_type)\n",
    "        self.model = mslite.Model()\n",
    "        self.model.build_from_file(model_path, mslite.ModelType.MINDIR, self.context)\n",
    "        self.ms_inputs = self.model.get_inputs()\n",
    "        self.use_ascend = use_ascend\n",
    "        self.onnx_runtime_model = onnx_runtime_model\n",
    "        self.get_shape = get_shape\n",
    "\n",
    "    @staticmethod\n",
    "    def init_context(device_type='ascend'):\n",
    "        context = mslite.Context()\n",
    "        context.target = [device_type]\n",
    "        context.ascend.device_id = int(os.getenv('DEVICE_ID') or 0)\n",
    "        context.cpu.thread_num = 1 if device_type == 'ascend' else 32\n",
    "        context.cpu.thread_affinity_mode = 2\n",
    "        return context\n",
    "\n",
    "    def __call__(self, **kwargs):\n",
    "        if not self.use_ascend:\n",
    "            return self.onnx_runtime_model(**kwargs)\n",
    "        inputs = list(kwargs.values())\n",
    "        if self.get_shape:\n",
    "            shapes = [list(input.shape) for input in inputs]\n",
    "            print(f\"{self.model_name}: shapes={shapes}\")\n",
    "        if len(inputs) <= 0:\n",
    "            raise Exception('get tensor input info failed')\n",
    "        ms_input = self.model.get_inputs()\n",
    "        for index, val in enumerate(ms_input):\n",
    "            val.set_data_from_numpy(inputs[index])\n",
    "        outputs = self.model.predict(ms_input)\n",
    "        outputs = [output.get_data_to_numpy() for output in outputs]\n",
    "        return outputs\n",
    "\n",
    "```\n",
    "\n",
    "```\n",
    "# 初始化多个模型\n",
    "self.text_encoder_ms =  self.init_mslite_model(text_encoder_path)\n",
    "self.vae_encoder_ms =  self.init_mslite_model(vae_encoder_path)\n",
    "...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfa8f70-8ea3-4fb2-8bc3-3265b1235ca1",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "以pipeline_onnx_stable_diffusion_img2img.py的推理为例，额外增加了对应的mslite的运行时初始化以及实现\n",
    "\n",
    "初始化推理引擎并加载模型，参考\n",
    "\n",
    "```python\n",
    "def mslite_modules_init(self):\n",
    "    print(\"get device_id: \", int(os.getenv('DEVICE_ID') or 0))\n",
    "    model_path = os.getenv('MODEL_PATH')\n",
    "    model_path = model_path if model_path else os.getcwd()\n",
    "    text_encoder_path = os.path.join(model_path, \"text_encoder.mindir\")\n",
    "    print(\"init mslite text_encoder\")\n",
    "    self.text_encoder_ms = MsliteModel(text_encoder_path, 'text_encoder', use_ascend=self.use_mslite, onnx_runtime_model= self.text_encoder)\n",
    "\n",
    "```\n",
    "替换推理模型\n",
    "```python\n",
    "# 修改前\n",
    "text_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n",
    "# 修改后\n",
    "text_embeddings = self.text_encoder_ms(input_ids=text_input_ids.astype(np.int32))[0]\n",
    "```\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111112c0-d2f0-4672-9f6d-f0506018eae6",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "推理应用开发和用户的业务以及pipeline是强相关的，这里我们参考stable diffusion的onnx推理的pipeline脚本，将使用onnx模型前向推理的流程替换成使用mindir模型的推理流程即可。\n",
    "\n",
    "实际代码如下所示，大部分代码都是官方的原始代码，只在模型加载和模型前向推理时增加了mindspore lite推理模型的实现。\n",
    "\n",
    "需要将修改后的代码替换原始的官方脚本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ba91a235-a0a7-45de-a39e-3f2f4082f3e1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing pipeline_onnx_stable_diffusion_img2img.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile pipeline_onnx_stable_diffusion_img2img.py\n",
    "\n",
    "# Copyright 2022 The HuggingFace Team. All rights reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import inspect\n",
    "from typing import Callable, List, Optional, Union\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import mindspore_lite as mslite\n",
    "import pdb\n",
    "import time\n",
    "import os\n",
    "\n",
    "import PIL\n",
    "from transformers import CLIPFeatureExtractor, CLIPTokenizer\n",
    "\n",
    "from ...configuration_utils import FrozenDict\n",
    "from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel\n",
    "from ...pipeline_utils import DiffusionPipeline\n",
    "from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\n",
    "from ...utils import PIL_INTERPOLATION, deprecate, logging\n",
    "from . import StableDiffusionPipelineOutput\n",
    "\n",
    "\n",
    "logger = logging.get_logger(__name__)  # pylint: disable=invalid-name\n",
    "\n",
    "\n",
    "class MsliteModel:\n",
    "    def __init__(self, model_path, model_name='ms model', device_type='ascend', use_ascend=True,\n",
    "                 onnx_runtime_model=None, get_shape=False) -> None:\n",
    "        print('model_path:{}'.format(model_path))\n",
    "        self.model_name = model_name\n",
    "        self.context = MsliteModel.init_context(device_type)\n",
    "        self.model = mslite.Model()\n",
    "        self.model.build_from_file(model_path, mslite.ModelType.MINDIR, self.context)\n",
    "        self.ms_inputs = self.model.get_inputs()\n",
    "        self.use_ascend = use_ascend\n",
    "        self.onnx_runtime_model = onnx_runtime_model\n",
    "        self.get_shape = get_shape\n",
    "\n",
    "    @staticmethod\n",
    "    def init_context(device_type='ascend'):\n",
    "        context = mslite.Context()\n",
    "        context.target = [device_type]\n",
    "        context.ascend.device_id = int(os.getenv('DEVICE_ID') or 0)\n",
    "        context.cpu.thread_num = 1 if device_type == 'ascend' else 32\n",
    "        context.cpu.thread_affinity_mode = 2\n",
    "        return context\n",
    "\n",
    "    def __call__(self, **kwargs):\n",
    "        if not self.use_ascend:\n",
    "            return self.onnx_runtime_model(**kwargs)\n",
    "        inputs = list(kwargs.values())\n",
    "        if self.get_shape:\n",
    "            shapes = [list(input.shape) for input in inputs]\n",
    "            print(f\"===={self.model_name}: shapes={shapes}\")\n",
    "        if len(inputs) <= 0:\n",
    "            raise Exception('get tensor input info failed')\n",
    "        ms_input = self.model.get_inputs()\n",
    "        try:\n",
    "            for index, val in enumerate(ms_input):\n",
    "                val.set_data_from_numpy(inputs[index])\n",
    "        except Exception as err:\n",
    "            print(f\"val:{val};\")\n",
    "            print(f\"input.shape:{inputs[index].shape}\")\n",
    "            print(f\"input:{inputs[index]}\")\n",
    "            raise\n",
    "        outputs = self.model.predict(ms_input)\n",
    "        outputs = [output.get_data_to_numpy() for output in outputs]\n",
    "        return outputs\n",
    "\n",
    "\n",
    "# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\n",
    "def preprocess(image):\n",
    "    if isinstance(image, torch.Tensor):\n",
    "        return image\n",
    "    elif isinstance(image, PIL.Image.Image):\n",
    "        image = [image]\n",
    "\n",
    "    if isinstance(image[0], PIL.Image.Image):\n",
    "        w, h = image[0].size\n",
    "        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32\n",
    "\n",
    "        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n",
    "        image = np.concatenate(image, axis=0)\n",
    "        image = np.array(image).astype(np.float32) / 255.0\n",
    "        image = image.transpose(0, 3, 1, 2)\n",
    "        image = 2.0 * image - 1.0\n",
    "        image = torch.from_numpy(image)\n",
    "    elif isinstance(image[0], torch.Tensor):\n",
    "        image = torch.cat(image, dim=0)\n",
    "    return image\n",
    "\n",
    "\n",
    "class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):\n",
    "    r\"\"\"\n",
    "    Pipeline for text-guided image to image generation using Stable Diffusion.\n",
    "\n",
    "    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n",
    "    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n",
    "\n",
    "    Args:\n",
    "        vae ([`AutoencoderKL`]):\n",
    "            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n",
    "        text_encoder ([`CLIPTextModel`]):\n",
    "            Frozen text-encoder. Stable Diffusion uses the text portion of\n",
    "            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n",
    "            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n",
    "        tokenizer (`CLIPTokenizer`):\n",
    "            Tokenizer of class\n",
    "            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n",
    "        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n",
    "        scheduler ([`SchedulerMixin`]):\n",
    "            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n",
    "            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n",
    "        safety_checker ([`StableDiffusionSafetyChecker`]):\n",
    "            Classification module that estimates whether generated images could be considered offensive or harmful.\n",
    "            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n",
    "        feature_extractor ([`CLIPFeatureExtractor`]):\n",
    "            Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n",
    "    \"\"\"\n",
    "    vae_encoder: OnnxRuntimeModel\n",
    "    vae_decoder: OnnxRuntimeModel\n",
    "    text_encoder: OnnxRuntimeModel\n",
    "    tokenizer: CLIPTokenizer\n",
    "    unet: OnnxRuntimeModel\n",
    "    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]\n",
    "    safety_checker: OnnxRuntimeModel\n",
    "    feature_extractor: CLIPFeatureExtractor\n",
    "\n",
    "    _optional_components = [\"safety_checker\", \"feature_extractor\"]\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        vae_encoder: OnnxRuntimeModel,\n",
    "        vae_decoder: OnnxRuntimeModel,\n",
    "        text_encoder: OnnxRuntimeModel,\n",
    "        tokenizer: CLIPTokenizer,\n",
    "        unet: OnnxRuntimeModel,\n",
    "        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n",
    "        safety_checker: OnnxRuntimeModel,\n",
    "        feature_extractor: CLIPFeatureExtractor,\n",
    "        requires_safety_checker: bool = True,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n",
    "            deprecation_message = (\n",
    "                f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n",
    "                f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n",
    "                \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n",
    "                \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n",
    "                \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n",
    "                \" file\"\n",
    "            )\n",
    "            deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n",
    "            new_config = dict(scheduler.config)\n",
    "            new_config[\"steps_offset\"] = 1\n",
    "            scheduler._internal_dict = FrozenDict(new_config)\n",
    "\n",
    "        if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n",
    "            deprecation_message = (\n",
    "                f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n",
    "                \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n",
    "                \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n",
    "                \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n",
    "                \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n",
    "            )\n",
    "            deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n",
    "            new_config = dict(scheduler.config)\n",
    "            new_config[\"clip_sample\"] = False\n",
    "            scheduler._internal_dict = FrozenDict(new_config)\n",
    "\n",
    "        if safety_checker is None and requires_safety_checker:\n",
    "            logger.warning(\n",
    "                f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n",
    "                \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n",
    "                \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n",
    "                \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n",
    "                \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n",
    "                \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n",
    "            )\n",
    "\n",
    "        if safety_checker is not None and feature_extractor is None:\n",
    "            raise ValueError(\n",
    "                \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n",
    "                \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n",
    "            )\n",
    "\n",
    "        self.register_modules(\n",
    "            vae_encoder=vae_encoder,\n",
    "            vae_decoder=vae_decoder,\n",
    "            text_encoder=text_encoder,\n",
    "            tokenizer=tokenizer,\n",
    "            unet=unet,\n",
    "            scheduler=scheduler,\n",
    "            safety_checker=safety_checker,\n",
    "            feature_extractor=feature_extractor,\n",
    "        )\n",
    "        self.register_to_config(requires_safety_checker=requires_safety_checker)\n",
    "\n",
    "        self.is_profiling = False\n",
    "        self.use_mslite = (os.getenv('USE_MSLITE')).lower() == \"true\"\n",
    "        self.mslite_modules_init()\n",
    "\n",
    "    def mslite_modules_init(self):\n",
    "        print(\"get device_id: \", int(os.getenv('DEVICE_ID') or 0))\n",
    "\n",
    "        model_path = os.getenv('MODEL_PATH')\n",
    "        model_path = model_path if model_path else os.getcwd()\n",
    "        text_encoder_path = os.path.join(model_path, \"text_encoder.mindir\")\n",
    "        vae_encoder_path = os.path.join(model_path, \"vae_encoder.mindir\")\n",
    "        unet_path = os.path.join(model_path, \"unet_graph.mindir\")\n",
    "        vae_decoder_path = os.path.join(model_path, \"vae_decoder.mindir\")\n",
    "        safety_checker_path = os.path.join(model_path, \"safety_checker.mindir\")\n",
    "\n",
    "        print(\"init mslite text_encoder\")\n",
    "        self.text_encoder_ms = MsliteModel(text_encoder_path, 'text_encoder', use_ascend=self.use_mslite, onnx_runtime_model= self.text_encoder)\n",
    "\n",
    "        print(\"init mslite vae_encoder\")\n",
    "        self.vae_encoder_ms = MsliteModel(vae_encoder_path, 'vae_encoder', use_ascend=self.use_mslite, onnx_runtime_model=self.vae_encoder)\n",
    "\n",
    "        print(\"init mslite unet\")\n",
    "        self.unet_ms = MsliteModel(unet_path, 'unet', use_ascend=self.use_mslite, onnx_runtime_model=self.unet)\n",
    "\n",
    "        print(\"init mslite vae_decoder\")\n",
    "        self.vae_decoder_ms = MsliteModel(vae_decoder_path, 'vae_decoder', use_ascend=self.use_mslite, onnx_runtime_model=self.vae_decoder)\n",
    "\n",
    "        print(\"init mslite safety_checker\")\n",
    "        self.safety_checker_ms = MsliteModel(safety_checker_path, 'safety_checker', use_ascend=self.use_mslite, onnx_runtime_model=self.safety_checker)\n",
    "\n",
    "\n",
    "    # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt\n",
    "    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):\n",
    "        r\"\"\"\n",
    "        Encodes the prompt into text encoder hidden states.\n",
    "\n",
    "        Args:\n",
    "            prompt (`str` or `list(int)`):\n",
    "                prompt to be encoded\n",
    "            num_images_per_prompt (`int`):\n",
    "                number of images that should be generated per prompt\n",
    "            do_classifier_free_guidance (`bool`):\n",
    "                whether to use classifier free guidance or not\n",
    "            negative_prompt (`str` or `List[str]`):\n",
    "                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n",
    "                if `guidance_scale` is less than `1`).\n",
    "        \"\"\"\n",
    "        batch_size = len(prompt) if isinstance(prompt, list) else 1\n",
    "\n",
    "        # get prompt text embeddings\n",
    "        text_inputs = self.tokenizer(\n",
    "            prompt,\n",
    "            padding=\"max_length\",\n",
    "            max_length=self.tokenizer.model_max_length,\n",
    "            truncation=True,\n",
    "            return_tensors=\"np\",\n",
    "        )\n",
    "        text_input_ids = text_inputs.input_ids\n",
    "        untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n",
    "\n",
    "        if not np.array_equal(text_input_ids, untruncated_ids):\n",
    "            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n",
    "            logger.warning(\n",
    "                \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n",
    "                f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n",
    "            )\n",
    "\n",
    "        text_embeddings = self.text_encoder_ms(input_ids=text_input_ids.astype(np.int32))[0]\n",
    "        text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=0)\n",
    "\n",
    "        # get unconditional embeddings for classifier free guidance\n",
    "        if do_classifier_free_guidance:\n",
    "            uncond_tokens: List[str]\n",
    "            if negative_prompt is None:\n",
    "                uncond_tokens = [\"\"] * batch_size\n",
    "            elif type(prompt) is not type(negative_prompt):\n",
    "                raise TypeError(\n",
    "                    f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n",
    "                    f\" {type(prompt)}.\"\n",
    "                )\n",
    "            elif isinstance(negative_prompt, str):\n",
    "                uncond_tokens = [negative_prompt] * batch_size\n",
    "            elif batch_size != len(negative_prompt):\n",
    "                raise ValueError(\n",
    "                    f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n",
    "                    f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n",
    "                    \" the batch size of `prompt`.\"\n",
    "                )\n",
    "            else:\n",
    "                uncond_tokens = negative_prompt\n",
    "\n",
    "            max_length = text_input_ids.shape[-1]\n",
    "            uncond_input = self.tokenizer(\n",
    "                uncond_tokens,\n",
    "                padding=\"max_length\",\n",
    "                max_length=max_length,\n",
    "                truncation=True,\n",
    "                return_tensors=\"np\",\n",
    "            )\n",
    "            uncond_embeddings = self.text_encoder_ms(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n",
    "            uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)\n",
    "\n",
    "            # For classifier free guidance, we need to do two forward passes.\n",
    "            # Here we concatenate the unconditional and text embeddings into a single batch\n",
    "            # to avoid doing two forward passes\n",
    "            text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])\n",
    "\n",
    "        return text_embeddings\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        prompt: Union[str, List[str]],\n",
    "        image: Union[np.ndarray, PIL.Image.Image] = None,\n",
    "        strength: float = 0.8,\n",
    "        num_inference_steps: Optional[int] = 50,\n",
    "        guidance_scale: Optional[float] = 7.5,\n",
    "        negative_prompt: Optional[Union[str, List[str]]] = None,\n",
    "        num_images_per_prompt: Optional[int] = 1,\n",
    "        eta: Optional[float] = 0.0,\n",
    "        generator: Optional[np.random.RandomState] = None,\n",
    "        output_type: Optional[str] = \"pil\",\n",
    "        return_dict: bool = True,\n",
    "        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n",
    "        callback_steps: Optional[int] = 1,\n",
    "        **kwargs,\n",
    "    ):\n",
    "        r\"\"\"\n",
    "        Function invoked when calling the pipeline for generation.\n",
    "\n",
    "        Args:\n",
    "            prompt (`str` or `List[str]`):\n",
    "                The prompt or prompts to guide the image generation.\n",
    "            image (`np.ndarray` or `PIL.Image.Image`):\n",
    "                `Image`, or tensor representing an image batch, that will be used as the starting point for the\n",
    "                process.\n",
    "            strength (`float`, *optional*, defaults to 0.8):\n",
    "                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n",
    "                will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n",
    "                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n",
    "                be maximum and the denoising process will run for the full number of iterations specified in\n",
    "                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n",
    "            num_inference_steps (`int`, *optional*, defaults to 50):\n",
    "                The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n",
    "                expense of slower inference. This parameter will be modulated by `strength`.\n",
    "            guidance_scale (`float`, *optional*, defaults to 7.5):\n",
    "                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n",
    "                `guidance_scale` is defined as `w` of equation 2. of [Imagen\n",
    "                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n",
    "                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n",
    "                usually at the expense of lower image quality.\n",
    "            negative_prompt (`str` or `List[str]`, *optional*):\n",
    "                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n",
    "                if `guidance_scale` is less than `1`).\n",
    "            num_images_per_prompt (`int`, *optional*, defaults to 1):\n",
    "                The number of images to generate per prompt.\n",
    "            eta (`float`, *optional*, defaults to 0.0):\n",
    "                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n",
    "                [`schedulers.DDIMScheduler`], will be ignored for others.\n",
    "            generator (`np.random.RandomState`, *optional*):\n",
    "                A np.random.RandomState to make generation deterministic.\n",
    "            output_type (`str`, *optional*, defaults to `\"pil\"`):\n",
    "                The output format of the generate image. Choose between\n",
    "                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n",
    "            return_dict (`bool`, *optional*, defaults to `True`):\n",
    "                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n",
    "                plain tuple.\n",
    "            callback (`Callable`, *optional*):\n",
    "                A function that will be called every `callback_steps` steps during inference. The function will be\n",
    "                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n",
    "            callback_steps (`int`, *optional*, defaults to 1):\n",
    "                The frequency at which the `callback` function will be called. If not specified, the callback will be\n",
    "                called at every step.\n",
    "\n",
    "        Returns:\n",
    "            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n",
    "            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n",
    "            When returning a tuple, the first element is a list with the generated images, and the second element is a\n",
    "            list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n",
    "            (nsfw) content, according to the `safety_checker`.\n",
    "        \"\"\"\n",
    "        message = \"Please use `image` instead of `init_image`.\"\n",
    "        init_image = deprecate(\"init_image\", \"0.12.0\", message, take_from=kwargs)\n",
    "        image = init_image or image\n",
    "\n",
    "        if isinstance(prompt, str):\n",
    "            batch_size = 1\n",
    "        elif isinstance(prompt, list):\n",
    "            batch_size = len(prompt)\n",
    "        else:\n",
    "            raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n",
    "\n",
    "        if strength < 0 or strength > 1:\n",
    "            raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n",
    "\n",
    "        if (callback_steps is None) or (\n",
    "            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n",
    "        ):\n",
    "            raise ValueError(\n",
    "                f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n",
    "                f\" {type(callback_steps)}.\"\n",
    "            )\n",
    "\n",
    "        if generator is None:\n",
    "            generator = np.random\n",
    "\n",
    "        # set timesteps\n",
    "        self.scheduler.set_timesteps(num_inference_steps)\n",
    "\n",
    "        image = preprocess(image).cpu().numpy()\n",
    "\n",
    "        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n",
    "        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n",
    "        # corresponds to doing no classifier free guidance.\n",
    "        do_classifier_free_guidance = guidance_scale > 1.0\n",
    "\n",
    "        text_embeddings = self._encode_prompt(\n",
    "            prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n",
    "        )\n",
    "\n",
    "        latents_dtype = text_embeddings.dtype\n",
    "        image = image.astype(latents_dtype)\n",
    "        # encode the init image into latents and scale the latents\n",
    "        init_latents = self.vae_encoder_ms(sample=image.astype(np.float32))[0]\n",
    "        init_latents = 0.18215 * init_latents\n",
    "\n",
    "        if isinstance(prompt, str):\n",
    "            prompt = [prompt]\n",
    "        if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:\n",
    "            # expand init_latents for batch_size\n",
    "            deprecation_message = (\n",
    "                f\"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n",
    "                \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n",
    "                \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n",
    "                \" your script to pass as many initial images as text prompts to suppress this warning.\"\n",
    "            )\n",
    "            deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n",
    "            additional_image_per_prompt = len(prompt) // init_latents.shape[0]\n",
    "            init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0)\n",
    "        elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:\n",
    "            raise ValueError(\n",
    "                f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts.\"\n",
    "            )\n",
    "        else:\n",
    "            init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)\n",
    "\n",
    "        # get the original timestep using init_timestep\n",
    "        offset = self.scheduler.config.get(\"steps_offset\", 0)\n",
    "        init_timestep = int(num_inference_steps * strength) + offset\n",
    "        init_timestep = min(init_timestep, num_inference_steps)\n",
    "\n",
    "        timesteps = self.scheduler.timesteps.numpy()[-init_timestep]\n",
    "        timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)\n",
    "\n",
    "        # add noise to latents using the timesteps\n",
    "        noise = generator.randn(*init_latents.shape).astype(latents_dtype)\n",
    "        init_latents = self.scheduler.add_noise(\n",
    "            torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)\n",
    "        )\n",
    "        init_latents = init_latents.numpy()\n",
    "\n",
    "        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n",
    "        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n",
    "        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n",
    "        # and should be between [0, 1]\n",
    "        accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n",
    "        extra_step_kwargs = {}\n",
    "        if accepts_eta:\n",
    "            extra_step_kwargs[\"eta\"] = eta\n",
    "\n",
    "        latents = init_latents\n",
    "\n",
    "        t_start = max(num_inference_steps - init_timestep + offset, 0)\n",
    "        timesteps = self.scheduler.timesteps[t_start:].numpy()\n",
    "\n",
    "        timestep_dtype = next(\n",
    "            (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n",
    "        )\n",
    "        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n",
    "\n",
    "        for i, t in enumerate(self.progress_bar(timesteps)):\n",
    "            # expand the latents if we are doing classifier free guidance\n",
    "            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n",
    "            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)\n",
    "            latent_model_input = latent_model_input.cpu().numpy()\n",
    "\n",
    "            # predict the noise residual\n",
    "            timestep = np.array([t], dtype=timestep_dtype)\n",
    "            noise_pred = self.unet_ms(\n",
    "                sample=latent_model_input, timestep=timestep.astype(np.float32), encoder_hidden_states=text_embeddings\n",
    "            )[0]\n",
    "\n",
    "            # perform guidance\n",
    "            if do_classifier_free_guidance:\n",
    "                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n",
    "                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n",
    "\n",
    "            # compute the previous noisy sample x_t -> x_t-1\n",
    "            scheduler_output = self.scheduler.step(\n",
    "                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n",
    "            )\n",
    "            latents = scheduler_output.prev_sample.numpy()\n",
    "\n",
    "            # call the callback, if provided\n",
    "            if callback is not None and i % callback_steps == 0:\n",
    "                callback(i, t, latents)\n",
    "\n",
    "        latents = 1 / 0.18215 * latents\n",
    "        # image = self.vae_decoder(latent_sample=latents)[0]\n",
    "        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1\n",
    "        image = np.concatenate(\n",
    "            [self.vae_decoder_ms(latent_sample=latents[i : i + 1].astype(np.float32))[0] for i in range(latents.shape[0])]\n",
    "        )\n",
    "\n",
    "        image = np.clip(image / 2 + 0.5, 0, 1)\n",
    "        image = image.transpose((0, 2, 3, 1))\n",
    "\n",
    "        if self.safety_checker is not None:\n",
    "            safety_checker_input = self.feature_extractor(\n",
    "                self.numpy_to_pil(image), return_tensors=\"np\"\n",
    "            ).pixel_values.astype(image.dtype)\n",
    "            # safety_checker does not support batched inputs yet\n",
    "            images, has_nsfw_concept = [], []\n",
    "            for i in range(image.shape[0]):\n",
    "                image_i, has_nsfw_concept_i = self.safety_checker_ms(\n",
    "                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n",
    "                )\n",
    "                images.append(image_i)\n",
    "                has_nsfw_concept.append(has_nsfw_concept_i[0])\n",
    "            image = np.concatenate(images)\n",
    "        else:\n",
    "            has_nsfw_concept = None\n",
    "\n",
    "        if output_type == \"pil\":\n",
    "            image = self.numpy_to_pil(image)\n",
    "\n",
    "        if not return_dict:\n",
    "            return (image, has_nsfw_concept)\n",
    "\n",
    "        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf092c9-059c-48f4-a2b8-559f76a84a4a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "将对应的文件替换到diffusers源码中用于实际的推理测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9102bf6-64ef-475c-b534-dc0f8c124d8a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mv ./pipeline_onnx_stable_diffusion_img2img.py ./diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6de21d-87c8-4214-8d31-0fd7c906a50e",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "下载用于测试验证的图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0f9b3d1-fe84-488d-801e-d62588fb8a83",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!wget https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg\n",
    "from IPython import display\n",
    "display.Image(\"sketch-mountains-input.jpeg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92cc6ad5-b961-47f0-8711-32d159d4e3f2",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**构建推理测试启动脚本**\n",
    "\n",
    "推理的大部分逻辑都在前面的pipeline中已经实现了，下面需要按照stable diffusion推理的逻辑启动推理任务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4305c28d-1a98-46a0-acef-c447a13592e5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting diffusion_img2img_infer.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile diffusion_img2img_infer.py\n",
    "\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "from dataclasses import dataclass\n",
    "\n",
    "import torch\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "from diffusers import OnnxStableDiffusionImg2ImgPipeline\n",
    "\n",
    "model_path = \"./torch_to_onnx\"\n",
    "local_images_path = \"./sketch-mountains-input.jpeg\"\n",
    "output_images_path = \"./outputs_ascend\"\n",
    "\n",
    "os.makedirs(output_images_path, exist_ok=True)\n",
    "os.environ['DEVICE_ID'] = \"0\"\n",
    "os.environ[\"MODEL_PATH\"] = \"./mindir\"\n",
    "\n",
    "def setup_seed(seed):\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class UNet2DConditionOutput:\n",
    "    sample: torch.FloatTensor\n",
    "\n",
    "def image2image(profiling=False):\n",
    "    setup_seed(0)\n",
    "    t1 = time.time()\n",
    "    print(\"StableDiffusionImg2ImgPipeline start\")\n",
    "    pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(\n",
    "        model_path,\n",
    "        # revision=\"fp16\",\n",
    "        torch_dtype=torch.float32,\n",
    "    )\n",
    "    print(\"StableDiffusionImg2ImgPipeline end\")\n",
    "\n",
    "    t2 = time.time()\n",
    "    print(f\"load model {t2-t1}\")\n",
    "    # inference example\n",
    "    infer_t = []\n",
    "    load_t = []\n",
    "    save_t = []\n",
    "    def infer():\n",
    "        t3 = time.time()\n",
    "        init_image = Image.open(local_images_path).convert(\"RGB\")\n",
    "        init_image = init_image.resize((512, 512))\n",
    "        t4 = time.time()\n",
    "        prompt = \"A fantasy landscape, trending on artstation\"\n",
    "        with torch.no_grad():\n",
    "            images = pipe(prompt, init_image, strength=0.77, guidance_scale=7.5,num_inference_steps=30).images\n",
    "        \n",
    "        t5 = time.time()\n",
    "        for i, image in enumerate(images):\n",
    "            save_img_path = output_images_path + \"/result_\" + str(i) + \".png\"\n",
    "            print(\"save path: \", save_img_path)\n",
    "            image.save(save_img_path)\n",
    "\n",
    "        t6 = time.time()\n",
    "        load_t.append(t4-t3)\n",
    "        infer_t.append(t5-t4)\n",
    "        save_t.append(t6-t5)\n",
    "        print(f\"infer {t4-t3}, {t5-t4}, {t6-t5}\")\n",
    "\n",
    "    if profiling:\n",
    "        num_warnup = 1\n",
    "        num_run = 2\n",
    "        print(\"Warmup: \" + \"---------\" * 20)\n",
    "        for _ in range(num_warnup):\n",
    "            infer()\n",
    "        print(\"Run: \" + \"---------\" * 20)\n",
    "        for _ in range(num_run):\n",
    "            infer()\n",
    "\n",
    "        load_avg = np.mean(load_t[num_warnup:])\n",
    "        infer_avg = np.mean(infer_t[num_warnup:])\n",
    "        save_avg = np.mean(save_t[num_warnup:])\n",
    "        print(f\"Avg time per image: {load_avg+infer_avg+save_avg}, load: {load_avg}, infer: {infer_avg}, save: {save_avg}\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    image2image(profiling=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134fcd83-a623-495c-ac61-d0fb8566b368",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "配置PYTHONPATH环境变量并执行推理脚本"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "671b3467-b7f4-4182-9e99-2f09f7fb2623",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "使用mindir模型在Ascend进行推理，设置USE_MSLITE环境变量为true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51ac1279-9c65-4f9b-b7a4-e1a9fc535045",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StableDiffusionImg2ImgPipeline start\n",
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/transformers/models/clip/feature_extraction_clip.py:31: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\n",
      "  FutureWarning,\n",
      "get device_id:  0\n",
      "init mslite text_encoder\n",
      "model_path:./mindir/text_encoder.mindir\n",
      "init mslite vae_encoder\n",
      "model_path:./mindir/vae_encoder.mindir\n",
      "init mslite unet\n",
      "model_path:./mindir/unet_graph.mindir\n",
      "init mslite vae_decoder\n",
      "model_path:./mindir/vae_decoder.mindir\n",
      "init mslite safety_checker\n",
      "model_path:./mindir/safety_checker.mindir\n",
      "StableDiffusionImg2ImgPipeline end\n",
      "load model 27.243826627731323\n",
      "Warmup: ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "100%|███████████████████████████████████████████| 24/24 [00:03<00:00,  7.72it/s]\n",
      "====vae_decoder: shapes=[[1, 4, 64, 64]]\n",
      "save path:  ./outputs_ascend/result_0.png\n",
      "infer 0.035347700119018555, 3.2682836055755615, 0.17370820045471191\n",
      "Run: ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "100%|███████████████████████████████████████████| 24/24 [00:03<00:00,  7.68it/s]\n",
      "====vae_decoder: shapes=[[1, 4, 64, 64]]\n",
      "save path:  ./outputs_ascend/result_0.png\n",
      "infer 0.011893749237060547, 3.2706685066223145, 0.18697500228881836\n",
      "100%|███████████████████████████████████████████| 24/24 [00:03<00:00,  7.73it/s]\n",
      "====vae_decoder: shapes=[[1, 4, 64, 64]]\n",
      "save path:  ./outputs_ascend/result_0.png\n",
      "infer 0.011848211288452148, 3.2484612464904785, 0.17236590385437012\n",
      "Avg time per image: 3.451106309890747, load: 0.011870980262756348, infer: 3.2595648765563965, save: 0.17967045307159424\n",
      "malloc_consolidate(): invalid chunk size\n"
     ]
    }
   ],
   "source": [
    "!export USE_MSLITE=true && \\\n",
    "export PYTHONPATH=$(pwd)/diffusers/src:$PYTHONPATH && \\\n",
    "python diffusion_img2img_infer.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae9d166-1572-4c02-98b3-47b554232bd7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "使用onnx模型在CPU进行推理，设置USE_MSLITE环境变量为false"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615d6f81-5395-4299-bb5c-8df41e7aded2",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "展示推理的图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c285304d-2374-4cad-8922-6ec00e0fd636",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"./outputs_ascend/result_0.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MindSpore",
   "language": "python",
   "name": "mindspore"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "02e42fcdf05a439d81ad6b5d16dbc4b8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "5bec36de2485400f8af1514012704857": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "71cba9d893bf44828204f4306e234ade": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "bar_style": "success",
       "description": "Fetching 15 files: 100%",
       "layout": "IPY_MODEL_e1fe921838a14488a04048610d9403d2",
       "max": 15,
       "style": "IPY_MODEL_c16830e45b28493fb3059dd3d0422663",
       "value": 15
      }
     },
     "9b10e503430c4086bcf3ca38c06b8a8b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_5bec36de2485400f8af1514012704857",
       "style": "IPY_MODEL_02e42fcdf05a439d81ad6b5d16dbc4b8",
       "value": " 15/15 [00:02&lt;00:00,  7.37it/s]"
      }
     },
     "c16830e45b28493fb3059dd3d0422663": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "dcf8977b39dc4581975aa68996b53f67": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "dd6c3e31d9b840c1885170d81ef43431": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_71cba9d893bf44828204f4306e234ade",
        "IPY_MODEL_9b10e503430c4086bcf3ca38c06b8a8b"
       ],
       "layout": "IPY_MODEL_dcf8977b39dc4581975aa68996b53f67"
      }
     },
     "e1fe921838a14488a04048610d9403d2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

一键复制编辑原始数据按行查看历史

提交于 2024-04-23 14:37 +08:00 . update model download address

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