philschmid HF staff commited on
Commit
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1 Parent(s): 384a620
create_handler.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "## Create Custom Handler for Inference Endpoints\n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 7,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Requirement already satisfied: diffusers in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (0.9.0)\n",
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+ "Collecting diffusers\n",
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+ " Using cached diffusers-0.10.2-py3-none-any.whl (503 kB)\n",
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+ "Requirement already satisfied: importlib-metadata in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (4.11.4)\n",
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+ "Requirement already satisfied: filelock in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (3.8.0)\n",
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+ "Requirement already satisfied: numpy in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (1.22.4)\n",
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+ "Requirement already satisfied: Pillow in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (9.2.0)\n",
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+ "Requirement already satisfied: huggingface-hub>=0.10.0 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (0.11.1)\n",
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+ "Requirement already satisfied: requests in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (2.28.1)\n",
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+ "Requirement already satisfied: regex!=2019.12.17 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from diffusers) (2022.7.25)\n",
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+ "Requirement already satisfied: packaging>=20.9 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from huggingface-hub>=0.10.0->diffusers) (21.3)\n",
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+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from huggingface-hub>=0.10.0->diffusers) (4.3.0)\n",
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+ "Requirement already satisfied: pyyaml>=5.1 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from huggingface-hub>=0.10.0->diffusers) (6.0)\n",
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+ "Requirement already satisfied: tqdm in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from huggingface-hub>=0.10.0->diffusers) (4.64.0)\n",
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+ "Requirement already satisfied: zipp>=0.5 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from importlib-metadata->diffusers) (3.8.1)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from requests->diffusers) (3.3)\n",
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+ "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from requests->diffusers) (2.1.0)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from requests->diffusers) (2022.6.15)\n",
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+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from requests->diffusers) (1.26.11)\n",
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+ "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages (from packaging>=20.9->huggingface-hub>=0.10.0->diffusers) (3.0.9)\n",
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+ "Installing collected packages: diffusers\n",
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+ " Attempting uninstall: diffusers\n",
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+ " Found existing installation: diffusers 0.9.0\n",
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+ " Uninstalling diffusers-0.9.0:\n",
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+ " Successfully uninstalled diffusers-0.9.0\n",
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+ "Successfully installed diffusers-0.10.2\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "!pip install diffusers --upgrade"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import torch\n",
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+ "\n",
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+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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+ "\n",
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+ "\n",
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+ "if device.type != 'cuda':\n",
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+ " raise ValueError(\"need to run on GPU\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Overwriting handler.py\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "%%writefile handler.py\n",
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+ "from typing import Dict, List, Any\n",
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+ "import torch\n",
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+ "from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline\n",
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+ "from PIL import Image\n",
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+ "import base64\n",
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+ "from io import BytesIO\n",
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+ "\n",
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+ "\n",
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+ "# set device\n",
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+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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+ "\n",
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+ "if device.type != 'cuda':\n",
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+ " raise ValueError(\"need to run on GPU\")\n",
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+ "\n",
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+ "class EndpointHandler():\n",
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+ " def __init__(self, path=\"\"):\n",
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+ " # load StableDiffusionInpaintPipeline pipeline\n",
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+ " self.pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16)\n",
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+ " # use DPMSolverMultistepScheduler\n",
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+ " self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)\n",
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+ " # move to device \n",
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+ " self.pipe = self.pipe.to(device)\n",
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+ "\n",
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+ "\n",
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+ " def __call__(self, data: Any) -> List[List[Dict[str, float]]]:\n",
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+ " \"\"\"\n",
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+ " :param data: A dictionary contains `inputs` and optional `image` field.\n",
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+ " :return: A dictionary with `image` field contains image in base64.\n",
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+ " \"\"\"\n",
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+ " inputs = data.pop(\"inputs\", data)\n",
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+ " encoded_image = data.pop(\"image\", None)\n",
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+ " encoded_mask_image = data.pop(\"mask_image\", None)\n",
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+ " \n",
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+ " # hyperparamters\n",
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+ " num_inference_steps = data.pop(\"num_inference_steps\", 25)\n",
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+ " guidance_scale = data.pop(\"guidance_scale\", 7.5)\n",
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+ " negative_prompt = data.pop(\"negative_prompt\", None)\n",
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+ " height = data.pop(\"height\", None)\n",
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+ " width = data.pop(\"width\", None)\n",
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+ " \n",
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+ " # process image\n",
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+ " if encoded_image is not None and encoded_mask_image is not None:\n",
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+ " image = self.decode_base64_image(encoded_image)\n",
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+ " mask_image = self.decode_base64_image(encoded_mask_image)\n",
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+ " else:\n",
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+ " image = None\n",
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+ " mask_image = None \n",
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+ " \n",
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+ " # run inference pipeline\n",
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+ " out = self.pipe(inputs, \n",
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+ " image=image, \n",
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+ " mask_image=mask_image, \n",
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+ " num_inference_steps=num_inference_steps,\n",
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+ " guidance_scale=guidance_scale,\n",
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+ " num_images_per_prompt=1,\n",
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+ " negative_prompt=negative_prompt,\n",
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+ " height=height,\n",
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+ " width=width\n",
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+ " )\n",
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+ " \n",
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+ " # return first generate PIL image\n",
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+ " return out.images[0]\n",
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+ " \n",
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+ " # helper to decode input image\n",
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+ " def decode_base64_image(self, image_string):\n",
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+ " base64_image = base64.b64decode(image_string)\n",
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+ " buffer = BytesIO(base64_image)\n",
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+ " image = Image.open(buffer)\n",
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+ " return image.convert(\"RGB\").thumbnail((768, 768))"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "The config attributes {'upcast_attention': False} were passed to UNet2DConditionModel, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "from handler import EndpointHandler\n",
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+ "\n",
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+ "# init handler\n",
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+ "my_handler = EndpointHandler(path=\".\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/tmp/ipykernel_12933/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">3313959780.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">26</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 26&gt;</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\">[Errno 2] No such file or directory: '/tmp/ipykernel_12933/3313959780.py'</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/endpoints/stable-diffusion-2-inpainting-endpoint/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">handler.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">50</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__call__</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47 │ │ │ </span>mask_image = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">48 │ │ </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">49 │ │ # run inference pipeline</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>50 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│ │ </span>out = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.pipe(inputs, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">51 │ │ │ │ │ │ </span>image=image, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">52 │ │ │ │ │ │ </span>mask_image=mask_image, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">53 │ │ │ │ │ │ </span>num_inference_steps=num_inference_steps, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/torch/autograd/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">grad_mode.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">27</span> in <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorate_context</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 24 │ │ </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">@functools</span>.wraps(func) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 25 │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorate_context</span>(*args, **kwargs): <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 26 │ │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.clone(): <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 27 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│ │ │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> func(*args, **kwargs) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 28 │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> cast(F, decorate_context) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 29 │ </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 30 │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_wrap_generator</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, func): <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/diffusers/pipelines/stable_diffusion</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">pipeline_stable_diffusion_inpaint.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">676</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__call__</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">673 │ │ </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">674 │ │ # 7. Prepare mask latent variables</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">675 │ │ </span>mask, masked_image_latents = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.prepare_mask_latents( <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>676 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│ │ │ </span>mask, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">677 │ │ │ </span>masked_image, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">678 │ │ │ </span>batch_size * num_images_per_prompt, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">679 │ │ │ </span>height, <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
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+ "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">UnboundLocalError: </span>local variable <span style=\"color: #008000; text-decoration-color: #008000\">'mask'</span> referenced before assignment\n",
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+ "</pre>\n"
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+ ],
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+ "text/plain": [
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+ "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/tmp/ipykernel_12933/\u001b[0m\u001b[1;33m3313959780.py\u001b[0m:\u001b[94m26\u001b[0m in \u001b[92m<cell line: 26>\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[3;31m[Errno 2] No such file or directory: '/tmp/ipykernel_12933/3313959780.py'\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/endpoints/stable-diffusion-2-inpainting-endpoint/\u001b[0m\u001b[1;33mhandler.py\u001b[0m:\u001b[94m50\u001b[0m in \u001b[92m__call__\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m47 \u001b[0m\u001b[2m│ │ │ \u001b[0mmask_image = \u001b[94mNone\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m48 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m49 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# run inference pipeline\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m50 \u001b[2m│ │ \u001b[0mout = \u001b[96mself\u001b[0m.pipe(inputs, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m51 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0mimage=image, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m52 \u001b[0m\u001b[2m�� │ │ │ │ │ \u001b[0mmask_image=mask_image, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m53 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0mnum_inference_steps=num_inference_steps, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/torch/autograd/\u001b[0m\u001b[1;33mgrad_mode.py\u001b[0m:\u001b[94m27\u001b[0m in \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[92mdecorate_context\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 24 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[1;95m@functools\u001b[0m.wraps(func) \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 25 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mdecorate_context\u001b[0m(*args, **kwargs): \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 26 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mwith\u001b[0m \u001b[96mself\u001b[0m.clone(): \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 27 \u001b[2m│ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m func(*args, **kwargs) \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 28 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m cast(F, decorate_context) \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 29 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 30 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_wrap_generator\u001b[0m(\u001b[96mself\u001b[0m, func): \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/diffusers/pipelines/stable_diffusion\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/\u001b[0m\u001b[1;33mpipeline_stable_diffusion_inpaint.py\u001b[0m:\u001b[94m676\u001b[0m in \u001b[92m__call__\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m673 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m674 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# 7. Prepare mask latent variables\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m675 \u001b[0m\u001b[2m│ │ \u001b[0mmask, masked_image_latents = \u001b[96mself\u001b[0m.prepare_mask_latents( \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m676 \u001b[2m│ │ │ \u001b[0mmask, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m677 \u001b[0m\u001b[2m│ │ │ \u001b[0mmasked_image, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m678 \u001b[0m\u001b[2m│ │ │ \u001b[0mbatch_size * num_images_per_prompt, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m679 \u001b[0m\u001b[2m│ │ │ \u001b[0mheight, \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
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+ "\u001b[1;91mUnboundLocalError: \u001b[0mlocal variable \u001b[32m'mask'\u001b[0m referenced before assignment\n"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "import base64\n",
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+ "from PIL import Image\n",
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+ "from io import BytesIO\n",
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+ "\n",
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+ "# helper image utils\n",
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+ "def encode_image(image_path):\n",
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+ " with open(image_path, \"rb\") as i:\n",
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+ " b64 = base64.b64encode(i.read())\n",
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+ " return b64.decode(\"utf-8\")\n",
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+ " \n",
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+ "def decode_image(image_string):\n",
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+ " base64_image = base64.b64decode(image_string)\n",
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+ " buffer = BytesIO(base64_image)\n",
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+ " image = Image.open(buffer)\n",
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+ " return image\n",
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+ "\n",
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+ "prompt = \"a high resulotion image of a seberian cat\"\n",
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+ "image = encode_image(\"dog.png\")\n",
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+ "mask_image = encode_image(\"mask_dog.png\")\n",
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+ "\n",
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+ "# prepare sample payload\n",
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+ "request = {\"inputs\": prompt, \"image\": image, \"mask_image\": mask_image}\n",
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+ "\n",
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+ "# test the handler\n",
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+ "pred = my_handler(request)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "decode_image(pred[\"image\"])"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "![test](sample.jpg)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 8,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "The config attributes {'upcast_attention': False} were passed to UNet2DConditionModel, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "text/html": [
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/tmp/ipykernel_12933/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">1159614552.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">16</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 16&gt;</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\">[Errno 2] No such file or directory: '/tmp/ipykernel_12933/1159614552.py'</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/torch/autograd/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">grad_mode.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">27</span> in <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorate_context</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 24 │ │ </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">@functools</span>.wraps(func) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 25 │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorate_context</span>(*args, **kwargs): <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 26 │ │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.clone(): <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 27 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│ │ │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> func(*args, **kwargs) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 28 │ │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> cast(F, decorate_context) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 29 │ </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 30 │ </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_wrap_generator</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, func): <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/diffusers/pipelines/stable_diffusion</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">pipeline_stable_diffusion_inpaint.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">676</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__call__</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">673 │ │ </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">674 │ │ # 11. Post-processing</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">675 │ │ </span>image = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.decode_latents(latents) <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>676 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│ │ </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">677 │ │ # 12. Run safety checker</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">678 │ │ </span>image, has_nsfw_concept = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.run_safety_checker(image, device, text_embeddings <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">679 </span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
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+ "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
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+ "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">UnboundLocalError: </span>local variable <span style=\"color: #008000; text-decoration-color: #008000\">'mask'</span> referenced before assignment\n",
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+ "</pre>\n"
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+ ],
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+ "text/plain": [
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+ "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/tmp/ipykernel_12933/\u001b[0m\u001b[1;33m1159614552.py\u001b[0m:\u001b[94m16\u001b[0m in \u001b[92m<cell line: 16>\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/torch/autograd/\u001b[0m\u001b[1;33mgrad_mode.py\u001b[0m:\u001b[94m27\u001b[0m in \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[92mdecorate_context\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 26 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mwith\u001b[0m \u001b[96mself\u001b[0m.clone(): \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 27 \u001b[2m│ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m func(*args, **kwargs) \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 28 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m cast(F, decorate_context) \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 29 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m 30 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_wrap_generator\u001b[0m(\u001b[96mself\u001b[0m, func): \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/miniconda/envs/dev/lib/python3.9/site-packages/diffusers/pipelines/stable_diffusion\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2;33m/\u001b[0m\u001b[1;33mpipeline_stable_diffusion_inpaint.py\u001b[0m:\u001b[94m676\u001b[0m in \u001b[92m__call__\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m674 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# 11. Post-processing\u001b[0m \u001b[31m│\u001b[0m\n",
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+ "\u001b[31m│\u001b[0m \u001b[2m675 \u001b[0m\u001b[2m│ │ \u001b[0mimage = \u001b[96mself\u001b[0m.decode_latents(latents) \u001b[31m│\u001b[0m\n",
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+ "\u001b[1;91mUnboundLocalError: \u001b[0mlocal variable \u001b[32m'mask'\u001b[0m referenced before assignment\n"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "from diffusers import StableDiffusionInpaintPipeline\n",
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+ "import torch\n",
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+ "from PIL import Image\n",
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+ "\n",
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+ "pipe = StableDiffusionInpaintPipeline.from_pretrained(\n",
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+ "\".\" , torch_dtype=torch.float16)\n",
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+ "pipe.to(\"cuda\")\n",
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+ "\n",
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+ "\n",
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+ "image = Image.open(\"dog.png\").convert(\"RGB\").thumbnail((768, 768))\n",
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+ "mask = Image.open(\"mask_dog.png\").convert(\"RGB\").thumbnail((768, 768))\n",
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+ "\n",
410
+ "prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n",
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+ "#image and mask_image should be PIL images.\n",
412
+ "#The mask structure is white for inpainting and black for keeping as is\n",
413
+ "image = pipe(prompt=prompt, image=image, mask_image=mask).images[0]\n",
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+ "image.save(\"./yellow_cat_on_park_bench.png\")"
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+ ]
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+ },
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dog.png ADDED
handler.py ADDED
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1
+ from typing import Dict, List, Any
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+ import torch
3
+ from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline
4
+ from PIL import Image
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+ import base64
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+ from io import BytesIO
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+
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+
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+ # set device
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+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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+
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+ if device.type != 'cuda':
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+ raise ValueError("need to run on GPU")
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+
15
+ class EndpointHandler():
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+ def __init__(self, path=""):
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+ # load StableDiffusionInpaintPipeline pipeline
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+ self.pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16)
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+ # use DPMSolverMultistepScheduler
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+ self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
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+ # move to device
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+ self.pipe = self.pipe.to(device)
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+
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+
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+ def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
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+ """
27
+ :param data: A dictionary contains `inputs` and optional `image` field.
28
+ :return: A dictionary with `image` field contains image in base64.
29
+ """
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+ inputs = data.pop("inputs", data)
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+ encoded_image = data.pop("image", None)
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+ encoded_mask_image = data.pop("mask_image", None)
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+
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+ # hyperparamters
35
+ num_inference_steps = data.pop("num_inference_steps", 25)
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+ guidance_scale = data.pop("guidance_scale", 7.5)
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+ negative_prompt = data.pop("negative_prompt", None)
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+ height = data.pop("height", None)
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+ width = data.pop("width", None)
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+
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+ # process image
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+ if encoded_image is not None and encoded_mask_image is not None:
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+ image = self.decode_base64_image(encoded_image)
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+ mask_image = self.decode_base64_image(encoded_mask_image)
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+ else:
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+ image = None
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+ mask_image = None
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+
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+ # run inference pipeline
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+ out = self.pipe(inputs,
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+ image=image,
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+ mask_image=mask_image,
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+ num_inference_steps=num_inference_steps,
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+ guidance_scale=guidance_scale,
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+ num_images_per_prompt=1,
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+ negative_prompt=negative_prompt,
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+ height=height,
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+ width=width
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+ )
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+
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+ # return first generate PIL image
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+ return out.images[0]
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+
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+ # helper to decode input image
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+ def decode_base64_image(self, image_string):
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+ base64_image = base64.b64decode(image_string)
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+ buffer = BytesIO(base64_image)
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+ image = Image.open(buffer)
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+ return image.convert("RGB").thumbnail((768, 768))
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