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Colab notebook for CogVideoX-2b
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "1gcBBbBCW_CV"
},
"outputs": [],
"source": [
"# Copyright 2025 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": "wKzYxAA1W_CV"
},
"source": [
"# Vertex AI Model Garden - CogVideoX-2b\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_xdit_cogvideox_2b.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_xdit_cogvideox_2b.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": "2WwEeH8BW_CV"
},
"source": [
"## Overview\n",
"\n",
"This notebook demonstrates deploying the pre-trained [CogVideoX-2b](https://huggingface.co/THUDM/CogVideoX-2b) 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), [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": "TAKAyLQvW_CV"
},
"source": [
"## Run the notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "sGzHHcL3W_CV"
},
"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",
"# @markdown 4. If you want to run predictions with A100 80GB or H100 GPUs, we recommend using the regions listed below. **NOTE:** Make sure you have associated quota in selected regions. Click the links to see your current quota for each GPU type: [Nvidia A100 80GB](https://console.cloud.google.com/iam-admin/quotas?metric=aiplatform.googleapis.com%2Fcustom_model_serving_nvidia_a100_80gb_gpus), [Nvidia H100 80GB](https://console.cloud.google.com/iam-admin/quotas?metric=aiplatform.googleapis.com%2Fcustom_model_serving_nvidia_h100_gpus).\n",
"\n",
"# @markdown > | Machine Type | Accelerator Type | Recommended Regions |\n",
"# @markdown | ----------- | ----------- | ----------- |\n",
"# @markdown | a2-ultragpu-1g | 1 NVIDIA_A100_80GB | us-central1, us-east4, europe-west4, asia-southeast1, us-east4 |\n",
"# @markdown | a3-highgpu-2g | 2 NVIDIA_H100_80GB | us-west1, asia-southeast1, europe-west4 |\n",
"# @markdown | a3-highgpu-4g | 4 NVIDIA_H100_80GB | us-west1, asia-southeast1, europe-west4 |\n",
"# @markdown | a3-highgpu-8g | 8 NVIDIA_H100_80GB | us-central1, us-east5, europe-west4, us-west1, asia-southeast1 |\n",
"\n",
"import datetime\n",
"import importlib\n",
"import os\n",
"import uuid\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",
"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, \"cogvideox-2b\")\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}[email protected]\"\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",
"models, endpoints = {}, {}\n",
"\n",
"! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
"\n",
"common_util = importlib.import_module(\n",
" \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "q36QziORW_CV"
},
"outputs": [],
"source": [
"# @title Deploy the model to Vertex for online predictions\n",
"\n",
"# @markdown This section uploads the [THUDM/CogVideoX-2b](https://huggingface.co/THUDM/CogVideoX-2b) model to Model Registry and deploys it on the Endpoint with the specified accelerator.\n",
"\n",
"# @markdown The deployment takes ~15-30 minutes to finish.\n",
"\n",
"model_id = \"THUDM/CogVideoX-2b\"\n",
"task = \"text-to-video\"\n",
"\n",
"accelerator_type = \"NVIDIA_A100_80GB\" # @param [\"NVIDIA_A100_80GB\", \"NVIDIA_H100_80GB\", \"2 NVIDIA_H100_80GB\", \"2 NVIDIA_L4\"]\n",
"\n",
"machine_type_map = {\n",
" \"NVIDIA_A100_80GB\": \"a2-ultragpu-1g\",\n",
" \"NVIDIA_H100_80GB\": \"a3-highgpu-1g\",\n",
" \"2 NVIDIA_H100_80GB\": \"a3-highgpu-2g\",\n",
" \"2 NVIDIA_L4\": \"g2-standard-24\"\n",
"}\n",
"\n",
"machine_type = machine_type_map.get(accelerator_type)\n",
"accelerator_count = 1\n",
"\n",
"if machine_type is \"a3-highgpu-2g\":\n",
" accelerator_type = \"NVIDIA_H100_80GB\"\n",
" accelerator_count = 2\n",
"elif machine_type is \"g2-standard-24\":\n",
" accelerator_type = \"NVIDIA_L4\"\n",
" accelerator_count = 2\n",
"\n",
"\n",
"# The pre-built serving docker image. It contains serving scripts and models.\n",
"SERVE_DOCKER_URI = \"us-docker.pkg.dev/deeplearning-platform-release/vertex-model-garden/xdit-serve.cu125.0-1.ubuntu2204.py310\"\n",
"\n",
"\n",
"def deploy_model(model_id, task, machine_type, accelerator_type, accelerator_count):\n",
" \"\"\"Create a Vertex AI Endpoint and deploy the specified model to the endpoint.\"\"\"\n",
" common_util.check_quota(\n",
" project_id=PROJECT_ID,\n",
" region=REGION,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
" is_for_training=False,\n",
" )\n",
"\n",
" model_name = model_id\n",
"\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",
"\n",
" # xDiT serving parameters\n",
" serving_env[\"N_GPUS\"] = accelerator_count\n",
" serving_env[\"ENABLE_SLICING\"] = \"true\"\n",
" serving_env[\"ENABLE_TILING\"] = \"true\"\n",
" if accelerator_count == 2:\n",
" serving_env[\"USE_CFG_PARALLEL\"] = \"true\"\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=\"/predict\",\n",
" serving_container_health_route=\"/health\",\n",
" serving_container_environment_variables=serving_env,\n",
" model_garden_source_model_name=\"publishers/thudm/models/cogvideox-2b\"\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",
" system_labels={\n",
" \"NOTEBOOK_NAME\": \"model_garden_xdit_cogvideox_2b.ipynb\"\n",
" )\n",
" return model, endpoint\n",
"\n",
"\n",
"models[\"model\"], endpoints[\"endpoint\"] = deploy_model(\n",
" model_id=model_id,\n",
" task=task,\n",
" machine_type=machine_type,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
")\n",
"\n",
"print(\"endpoint_name:\", endpoints[\"endpoint\"].name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "TKJsEJoeW_CV"
},
"outputs": [],
"source": [
"# @title Predict\n",
"\n",
"# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts.\n",
"\n",
"# @markdown The inference takes ~70s with 1 A100 GPU.\n",
"\n",
"# @markdown The inference takes ~40s with 1 H100 GPU.\n",
"\n",
"# @markdown The inference takes ~18s with 2 H100 GPU\n",
"\n",
"# @markdown The inference takes ~110s with 2 L4 GPU.\n",
"\n",
"# @markdown Example:\n",
"\n",
"# @markdown ```\n",
"# @markdown text: A cat waving a sign that says hello world\n",
"# @markdown ```\n",
"\n",
"# @markdown You may adjust the parameters below to achieve best video quality.\n",
"\n",
"text = \"A cat waving a sign that says hello world\" # @param {type: \"string\"}\n",
"num_inference_steps = 50 # @param {type:\"number\"}\n",
"\n",
"instances = [{\"text\": text}]\n",
"parameters = {\n",
" \"num_inference_steps\": num_inference_steps,\n",
"}\n",
"\n",
"\n",
"response = endpoints[\"endpoint\"].predict(instances=instances, parameters=parameters)\n",
"\n",
"video_bytes = response.predictions[0][\"output\"]\n",
"\n",
"video_html = f\"\"\"\n",
"<video width=\"720\" height=\"480\" controls>\n",
"<source src=\"data:video/mp4;base64,{video_bytes}\" type=\"video/mp4\">\n",
"Your browser does not support the video tag.\n",
"</video>\n",
"\"\"\" # Assumes MP4. Change type if needed (e.g., video/webm)\n",
"\n",
"display(HTML(video_html))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "42leJGJFW_CV"
},
"outputs": [],
"source": [
"# @title Clean up resources\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"
]
}
],
"metadata": {
"colab": {
"name": "model_garden_xdit_cogvideox_2b.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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