notebooks/community/model_garden/model_garden_pytorch_instant_id.ipynb

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  "cells": [
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      "cell_type": "code",
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      "metadata": {
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      "source": [
        "# Copyright 2024 Google LLC\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",
        "#     https://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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99c1c3fc2ca5"
      },
      "source": [
        "# Vertex AI Model Garden - Instant ID\n",
        "\n",
        "<table><tbody><tr>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_instant_id.ipynb\">\n",
        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_instant_id.ipynb\">\n",
        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</tr></tbody></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3de7470326a2"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook demonstrates deploying the [InstantX/InstantID](https://huggingface.co/InstantX/InstantID) model on Vertex AI for online prediction.\n",
        "\n",
        "### Objective\n",
        "\n",
        "- Deploy the model to a [Vertex AI Endpoint resource](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
        "- Run online predictions for text-to-image.\n",
        "\n",
        "### Costs\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI\n",
        "* Cloud Storage\n",
        "\n",
        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "264c07757582"
      },
      "source": [
        "## Before you begin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "855d6b96f291"
      },
      "outputs": [],
      "source": [
        "# @title Setup Google Cloud project\n",
        "\n",
        "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
        "# @markdown 2. **[Optional]** [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
        "\n",
        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
        "\n",
        "# @markdown 3. **[Optional]** Set region. If not set, the region will be set automatically according to Colab Enterprise environment.\n",
        "\n",
        "REGION = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "# Import the necessary packages\n",
        "import datetime\n",
        "import importlib\n",
        "import os\n",
        "import uuid\n",
        "\n",
        "from google.cloud import aiplatform\n",
        "\n",
        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
        "common_util = importlib.import_module(\n",
        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
        ")\n",
        "\n",
        "models, endpoints = {}, {}\n",
        "\n",
        "\n",
        "# Get the default cloud project id.\n",
        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
        "\n",
        "# Get the default region for launching jobs.\n",
        "if not REGION:\n",
        "    REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
        "\n",
        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
        "\n",
        "# Cloud Storage bucket for storing the experiment artifacts.\n",
        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
        "# prefer using your own GCS bucket, change the value yourself below.\n",
        "now = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
        "\n",
        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
        "else:\n",
        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
        "    bucket_region = shell_output[0].strip().lower()\n",
        "    if bucket_region != REGION:\n",
        "        raise ValueError(\n",
        "            \"Bucket region %s is different from notebook region %s\"\n",
        "            % (bucket_region, REGION)\n",
        "        )\n",
        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
        "\n",
        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"instant_id\")\n",
        "\n",
        "\n",
        "# Initialize Vertex AI API.\n",
        "print(\"Initializing Vertex AI API.\")\n",
        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
        "\n",
        "# Gets the default SERVICE_ACCOUNT.\n",
        "shell_output = ! gcloud projects describe $PROJECT_ID\n",
        "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
        "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
        "\n",
        "\n",
        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
        "\n",
        "! gcloud config set project $PROJECT_ID\n",
        "! gcloud projects add-iam-policy-binding --no-user-output-enabled {PROJECT_ID} --member=serviceAccount:{SERVICE_ACCOUNT} --role=\"roles/storage.admin\"\n",
        "! gcloud projects add-iam-policy-binding --no-user-output-enabled {PROJECT_ID} --member=serviceAccount:{SERVICE_ACCOUNT} --role=\"roles/aiplatform.user\"\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_lR80GjCnEL4"
      },
      "source": [
        "## Deploy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "1cc26e68d7b0"
      },
      "outputs": [],
      "source": [
        "# @title Deploy the InstantID model to Vertex for online predictions\n",
        "\n",
        "# @markdown This section uploads the model to Model Registry and deploys it on the Endpoint. It takes ~15 minutes to finish.\n",
        "# @markdown Click \"Show Code\" to see more details.\n",
        "\n",
        "model_id = \"instantx/instantid\"\n",
        "task = \"instant-id\"\n",
        "\n",
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-diffusers-serve-opt:20240605_1400_RC00\"\n",
        "\n",
        "ACCELERATOR_TYPE = \"NVIDIA_L4\"\n",
        "ACCELERATOR_COUNT = 1\n",
        "MACHINE_TYPE = \"g2-standard-8\"\n",
        "\n",
        "\n",
        "def deploy_model(model_id, task, accelerator_type, machine_type, accelerator_count=1):\n",
        "    \"\"\"Create a Vertex AI Endpoint and deploy the specified model to the endpoint.\"\"\"\n",
        "    model_name = model_id\n",
        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-{task}-endpoint\")\n",
        "    serving_env = {\n",
        "        \"MODEL_ID\": model_id,\n",
        "        \"TASK\": task,\n",
        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
        "    }\n",
        "\n",
        "    model = aiplatform.Model.upload(\n",
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "    )\n",
        "\n",
        "    model.deploy(\n",
        "        endpoint=endpoint,\n",
        "        machine_type=machine_type,\n",
        "        accelerator_type=accelerator_type,\n",
        "        accelerator_count=accelerator_count,\n",
        "        deploy_request_timeout=1800,\n",
        "        service_account=SERVICE_ACCOUNT,\n",
        "    )\n",
        "    print(\"To load this existing endpoint from a different session:\")\n",
        "    print(\n",
        "        f'endpoint = aiplatform.Endpoint(\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint.name}\")'\n",
        "    )\n",
        "    return model, endpoint\n",
        "\n",
        "\n",
        "common_util.check_quota(\n",
        "    project_id=PROJECT_ID,\n",
        "    region=REGION,\n",
        "    accelerator_type=\"NVIDIA_L4\",\n",
        "    accelerator_count=1,\n",
        "    is_for_training=False,\n",
        ")\n",
        "\n",
        "\n",
        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model(\n",
        "    model_id=model_id,\n",
        "    task=task,\n",
        "    accelerator_type=ACCELERATOR_TYPE,\n",
        "    machine_type=MACHINE_TYPE,\n",
        ")\n",
        "print(\"endpoint_name:\", endpoints[\"vllm_gpu\"].name)\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "BXOk6dMtWjnW"
      },
      "outputs": [],
      "source": [
        "# @title Predict #1 - single reference face image\n",
        "\n",
        "# @markdown We support two prediction tasks:\n",
        "# @markdown - InstantId based on a single reference face image. The generated image\n",
        "# @markdown   has a similar face appearance and pose as the reference image.\n",
        "# @markdown - InstantId based on a reference face image, and a reference pose image. The generated\n",
        "# @markdown   image has a similar face appearance to the face image, and a pose similar to the pose image.\n",
        "\n",
        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts.\n",
        "\n",
        "# Loads an existing endpoint instance using the endpoint name:\n",
        "# - Using `endpoint_name = endpoint.name` allows us to get the endpoint name of\n",
        "#   the endpoint `endpoint` created in the cell above.\n",
        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
        "#   an existing endpoint with the ID 1234567890123456789.\n",
        "\n",
        "# You may uncomment the code below to load an existing endpoint.\n",
        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
        "# aip_endpoint_name = (\n",
        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
        "# )\n",
        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
        "# print(\"Using this existing endpoint from a different session: {aip_endpoint_name}\")\n",
        "\n",
        "# @markdown You may adjust the parameters below to achieve best image quality.\n",
        "\n",
        "\n",
        "prompt = \"A photo of a man wearing a red t-shirt, crisp, detailed, 8k\"  # @param {type: \"string\"}\n",
        "negative_prompt = \"blue, low-quality, deformed\"  # @param {type: \"string\"}\n",
        "height = 1024  # @param {type:\"integer\"}\n",
        "width = 1024  # @param {type:\"number\"}\n",
        "num_inference_steps = 5  # @param {type:\"number\"}\n",
        "guidance_scale = 1.3  # @param {type:\"number\"}\n",
        "\n",
        "reference_face_image_url = \"https://raw.githubusercontent.com/InstantID/InstantID/main/examples/yann-lecun_resize.jpg\"  # @param {type: \"string\"}\n",
        "face_image = common_util.download_image(reference_face_image_url)\n",
        "\n",
        "# Run prediction task #1: using a single face image\n",
        "instances = [\n",
        "    {\n",
        "        \"prompt\": prompt,\n",
        "        \"negative_prompt\": negative_prompt,\n",
        "        \"height\": height,\n",
        "        \"width\": width,\n",
        "        \"num_inference_steps\": num_inference_steps,\n",
        "        \"guidance_scale\": guidance_scale,\n",
        "        \"face_image\": common_util.image_to_base64(face_image),\n",
        "    },\n",
        "]\n",
        "\n",
        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
        "images = [common_util.base64_to_image(image) for image in response.predictions]\n",
        "display(common_util.image_grid(images, rows=1))\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "ef10b868f12d"
      },
      "outputs": [],
      "source": [
        "# @title Predict #2 - a reference face image and a reference pose image\n",
        "\n",
        "# You may uncomment the code below to load an existing endpoint.\n",
        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
        "# aip_endpoint_name = (\n",
        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
        "# )\n",
        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
        "# print(\"Using this existing endpoint from a different session: {aip_endpoint_name}\")\n",
        "\n",
        "# @markdown You may adjust the parameters below to achieve best image quality.\n",
        "\n",
        "reference_face_image_url = \"https://raw.githubusercontent.com/InstantID/InstantID/main/examples/yann-lecun_resize.jpg\"  # @param {type: \"string\"}\n",
        "reference_pose_image_url = \"https://raw.githubusercontent.com/InstantID/InstantID/main/examples/poses/pose2.jpg\"  # @param {type: \"string\"}\n",
        "face_image = common_util.download_image(reference_face_image_url)\n",
        "pose_image = common_util.download_image(reference_pose_image_url)\n",
        "\n",
        "instances = [\n",
        "    {\n",
        "        \"prompt\": prompt,\n",
        "        \"negative_prompt\": negative_prompt,\n",
        "        \"height\": height,\n",
        "        \"width\": width,\n",
        "        \"num_inference_steps\": num_inference_steps,\n",
        "        \"guidance_scale\": guidance_scale,\n",
        "        \"face_image\": common_util.image_to_base64(face_image),\n",
        "        \"pose_image\": common_util.image_to_base64(pose_image),\n",
        "    },\n",
        "]\n",
        "\n",
        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
        "images = [common_util.base64_to_image(image) for image in response.predictions]\n",
        "display(common_util.image_grid(images, rows=1))\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL_BpAiWm8fq"
      },
      "source": [
        "## Clean up resources"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "911406c1561e"
      },
      "outputs": [],
      "source": [
        "# @title Delete the model and endpoint\n",
        "\n",
        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
        "# @markdown  and avoid unnecessary continuous charges that may incur.\n",
        "\n",
        "# Undeploy model and delete endpoint.\n",
        "for endpoint in endpoints.values():\n",
        "    endpoint.delete(force=True)\n",
        "\n",
        "# Delete models.\n",
        "for model in models.values():\n",
        "    model.delete()\n",
        "\n",
        "delete_bucket = False  # @param {type:\"boolean\"}\n",
        "if delete_bucket:\n",
        "    ! gsutil -m rm -r $BUCKET_NAME"
      ]
    }
  ],
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      "name": "model_garden_pytorch_instant_id.ipynb",
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