Remove the text-to-video notebook as we decide to hide the model card from the UI.

2026-09-26 14:42:04 +00:00 · 2024-07-29 14:19:33 -07:00
1 changed files with 0 additions and 291 deletions
@@ -1,291 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Ug_ZXeBdbFI4"
-      },
-      "outputs": [],
-      "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."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Jr2jRuqabG1m"
-      },
-      "source": [
-        "# Vertex AI Model Garden - Text To Video\n",
-        "\n",
-        "<table align=\"left\"><tbody><tr>\n",
-        "  <td>\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_text_to_video.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>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_text_to_video.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
-        "      View on GitHub\n",
-        "    </a>\n",
-        "  </td>\n",
-        "</tr></tbody></table>"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wLLfRT_6bTZO"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "This notebook demonstrates deploying the pre-trained [Text To Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/text_to_video) model on Vertex AI for online prediction.\n",
-        "\n",
-        "### Objective\n",
-        "\n",
-        "- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
-        "- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
-        "- Run online predictions for text-to-video.\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) and [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": "c8b29e68bb87"
-      },
-      "source": [
-        "## Run the notebook"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "dbf16ae5574d"
-      },
-      "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",
-        "import os\n",
-        "import sys\n",
-        "import uuid\n",
-        "from datetime import datetime\n",
-        "\n",
-        "from google.cloud import aiplatform\n",
-        "from IPython.display import HTML\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",
-        "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
-        "\n",
-        "# Enable the Vertex AI API and Compute Engine API, if not already.\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, please change the value yourself below.\n",
-        "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
-        "BUCKET_URI = \"gs://\"  # @param {type: \"string\"}\n",
-        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
-        "\n",
-        "# Create a unique GCS bucket for this notebook, if not specified by the user.\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",
-        "\n",
-        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
-        "\n",
-        "# Set up the default SERVICE_ACCOUNT.\n",
-        "SERVICE_ACCOUNT = None\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",
-        "\n",
-        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\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",
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)\n",
-        "\n",
-        "if \"google.colab\" in sys.modules:\n",
-        "    from google.colab import auth\n",
-        "\n",
-        "    auth.authenticate_user(project_id=PROJECT_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",
-        "\n",
-        "def deploy_model(model_id, task):\n",
-        "    model_name = \"text-to-video\"\n",
-        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
-        "    serving_env = {\n",
-        "        \"MODEL_ID\": model_id,\n",
-        "        \"TASK\": task,\n",
-        "        \"DEPLOY_SOURCE\": \"notebook\",\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",
-        "    model.deploy(\n",
-        "        endpoint=endpoint,\n",
-        "        machine_type=\"g2-standard-8\",\n",
-        "        accelerator_type=\"NVIDIA_L4\",\n",
-        "        accelerator_count=1,\n",
-        "        deploy_request_timeout=1800,\n",
-        "    )\n",
-        "    return model, endpoint"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "fc3a118f0927"
-      },
-      "outputs": [],
-      "source": [
-        "# @title Upload and deploy models\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, endpoint = deploy_model(\n",
-        "    model_id=\"damo-vilab/text-to-video-ms-1.7b\", task=\"text-to-video\"\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "a927dfe74a71"
-      },
-      "outputs": [],
-      "source": [
-        "# @title Predict\n",
-        "\n",
-        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts to generate videos.\n",
-        "\n",
-        "# @markdown When deployed on one L4 GPU (the default machine type), the averaged inference time of a request is ~15 seconds.\n",
-        "\n",
-        "# @markdown Example:\n",
-        "\n",
-        "# @markdown ```\n",
-        "# @markdown Prompt: Spiderman is surfing\n",
-        "# @markdown ```\n",
-        "\n",
-        "# @markdown You may adjust the parameters below to achieve best video quality.\n",
-        "\n",
-        "prompt = \"Spiderman is surfing\"  # @param {type: \"string\"}\n",
-        "number_inference_steps = 25  # @param {type:\"number\"}\n",
-        "\n",
-        "instances = [\n",
-        "    {\"prompt\": prompt, \"number_inference_steps\": number_inference_steps},\n",
-        "]\n",
-        "response = endpoint.predict(instances=instances)\n",
-        "\n",
-        "html = \"\"\n",
-        "for video in response.predictions:\n",
-        "    html += \"<video controls>\"\n",
-        "    html += f'<source src=\"data:video/mp4;base64,{video}\" type=\"video/mp4\">'\n",
-        "    html += \"</video>\"\n",
-        "HTML(html)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "2ccf3714dbe9"
-      },
-      "outputs": [],
-      "source": [
-        "# @title Clean up resources\n",
-        "\n",
-        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
-        "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
-        "\n",
-        "try:\n",
-        "    # Undeploy model and delete endpoint.\n",
-        "    endpoint.delete(force=True)\n",
-        "\n",
-        "    # Delete model.\n",
-        "    model.delete()\n",
-        "\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
-        "\n",
-        "# Delete bucket.\n",
-        "delete_bucket = False  # @param {type:\"boolean\"}\n",
-        "if delete_bucket:\n",
-        "    ! gsutil -m rm -r $BUCKET_NAME"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "model_garden_pytorch_text_to_video.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}