{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3306ed06",
   "metadata": {},
   "source": [
    "## 1. 注册及token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c132f4d2",
   "metadata": {},
   "source": [
    "- 注册：\n",
    "    - https://huggingface.co\n",
    "- token\n",
    "    - https://huggingface.co/settings/tokens\n",
    "\n",
    "- 安装 huggingface 命令行工具：\n",
    "    - `$ python -m pip install huggingface_hub`\n",
    "    - `$ huggingface-cli login`\n",
    "\n",
    "- huggingface 获取 CompVis 相关模型的访问权限\n",
    "    - https://huggingface.co/CompVis/stable-diffusion-v1-4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e0b5e9",
   "metadata": {},
   "source": [
    "## 2. github 项目"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ee18a3",
   "metadata": {},
   "source": [
    "- git clone 到本地\n",
    "    - `git clone https://github.com/CompVis/stable-diffusion`\n",
    "- 配置虚拟环境 `ldm`（latent diffusion model）\n",
    " \n",
    "```\n",
    "conda env create -f environment.yaml\n",
    "conda activate ldm\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e4ea71",
   "metadata": {},
   "source": [
    "## 3. 生成第一个图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bca3d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chzhang/anaconda3/envs/ldm/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from torch import autocast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83d22a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "from diffusers import StableDiffusionPipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eac64ab0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching 16 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 29127.11it/s]\n",
      "The config attributes {'clip_sample': False} were passed to PNDMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n"
     ]
    }
   ],
   "source": [
    "pipe = StableDiffusionPipeline.from_pretrained(\n",
    "\t\"CompVis/stable-diffusion-v1-4\",\n",
    "#     revision='fp32',\n",
    "#     revision='fp16'\n",
    "\tuse_auth_token=True\n",
    ").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55d7a9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StableDiffusionPipeline {\n",
       "  \"_class_name\": \"StableDiffusionPipeline\",\n",
       "  \"_diffusers_version\": \"0.6.0\",\n",
       "  \"feature_extractor\": [\n",
       "    \"transformers\",\n",
       "    \"CLIPFeatureExtractor\"\n",
       "  ],\n",
       "  \"safety_checker\": [\n",
       "    \"stable_diffusion\",\n",
       "    \"StableDiffusionSafetyChecker\"\n",
       "  ],\n",
       "  \"scheduler\": [\n",
       "    \"diffusers\",\n",
       "    \"PNDMScheduler\"\n",
       "  ],\n",
       "  \"text_encoder\": [\n",
       "    \"transformers\",\n",
       "    \"CLIPTextModel\"\n",
       "  ],\n",
       "  \"tokenizer\": [\n",
       "    \"transformers\",\n",
       "    \"CLIPTokenizer\"\n",
       "  ],\n",
       "  \"unet\": [\n",
       "    \"diffusers\",\n",
       "    \"UNet2DConditionModel\"\n",
       "  ],\n",
       "  \"vae\": [\n",
       "    \"diffusers\",\n",
       "    \"AutoencoderKL\"\n",
       "  ]\n",
       "}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f07fd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = 'a photo of a chinese woman riding a horse on mars'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a30d00a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51/51 [00:12<00:00,  4.19it/s]\n"
     ]
    }
   ],
   "source": [
    "with autocast('cuda'):\n",
    "    output = pipe(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "547d0e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StableDiffusionPipelineOutput(images=[<PIL.Image.Image image mode=RGB size=512x512 at 0x7F5E81449CD0>], nsfw_content_detected=[False])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "679a12f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<PIL.Image.Image image mode=RGB size=512x512>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output['images']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "668beeef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca824027",
   "metadata": {},
   "outputs": [],
   "source": [
    "output['images'][0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01f4eee8",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'output' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43moutput\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimages\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./output/1.png\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'output' is not defined"
     ]
    }
   ],
   "source": [
    "output['images'][0].save('./output/1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "479e3705",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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