create_handler.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup & Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile requirements.txt\n",
    "diffusers==0.2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create Custom Handler for Inference Endpoints\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "if device.type != 'cuda':\n",
    "    raise ValueError(\"need to run on GPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting handler.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile handler.py\n",
    "from typing import  Dict, List, Any\n",
    "import torch\n",
    "from torch import autocast\n",
    "from diffusers import StableDiffusionPipeline\n",
    "import base64\n",
    "from io import BytesIO\n",
    "\n",
    "\n",
    "# set device\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "if device.type != 'cuda':\n",
    "    raise ValueError(\"need to run on GPU\")\n",
    "\n",
    "class EndpointHandler():\n",
    "    def __init__(self, path=\"\"):\n",
    "        # load the optimized model\n",
    "        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)\n",
    "        self.pipe = self.pipe.to(device)\n",
    "\n",
    "\n",
    "    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            data (:obj:):\n",
    "                includes the input data and the parameters for the inference.\n",
    "        Return:\n",
    "            A :obj:`dict`:. base64 encoded image\n",
    "        \"\"\"\n",
    "        inputs = data.pop(\"inputs\", data)\n",
    "        \n",
    "        # run inference pipeline\n",
    "        with autocast(device.type):\n",
    "            image = self.pipe(inputs, guidance_scale=7.5)[\"sample\"][0]  \n",
    "            \n",
    "        # encode image as base 64\n",
    "        buffered = BytesIO()\n",
    "        image.save(buffered, format=\"JPEG\")\n",
    "        img_str = base64.b64encode(buffered.getvalue())\n",
    "\n",
    "        # postprocess the prediction\n",
    "        return {\"image\": img_str.decode()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test custom pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.11.0+cu113'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n"
     ]
    }
   ],
   "source": [
    "from handler import EndpointHandler\n",
    "\n",
    "# init handler\n",
    "my_handler = EndpointHandler(path=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "376de150f16b4b4bb0c3ab8c513de5c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import base64\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import json\n",
    "\n",
    "# helper decoder\n",
    "def decode_base64_image(image_string):\n",
    "  base64_image = base64.b64decode(image_string)\n",
    "  buffer = BytesIO(base64_image)\n",
    "  return  Image.open(buffer)\n",
    "\n",
    "# prepare sample payload\n",
    "request = {\"inputs\": \"a high resulotion image of a macbook\"}\n",
    "\n",
    "# test the handler\n",
    "pred = my_handler(request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "decode_base64_image(pred[\"image\"]).save(\"sample.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![test](sample.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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