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Generate videos using text prompts

You can use Veo on Vertex AI to generate new videos from a text prompt using the Google Cloud console, the REST API, or the Vertex AI SDK for Python.

For more information about writing effective text prompts for video generation, see the Veo prompt guide.

This guide shows how to generate videos from text prompts using Veo, covering the following topics:

Before you begin: Set up your project and authentication to use Veo.
Generate videos from text: Use the Google Cloud console, the REST API, or the Vertex AI SDK for Python to create a video from a text description.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Enable the Vertex AI API.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Enable the Vertex AI API.

Enable the API

Set up authentication for your environment.

Select the tab for how you plan to use the samples on this page:
Console

When you use the Google Cloud console to access Google Cloud services and APIs, you don't need to set up authentication.
REST

To use the REST API samples on this page in a local development environment, you use the credentials you provide to the gcloud CLI.
For more information, see Authenticate for using REST in the Google Cloud authentication documentation.

Generate videos from text

You can generate videos using the Google Cloud console, the REST API, or the Vertex AI SDK for Python. Choose the interface that best suits your use case:

Interface	Description	Use Case
Google Cloud console	A web-based graphical interface that lets you generate videos by entering prompts and configuring settings directly in your browser.	Best for experimenting with different prompts, models, and settings without writing any code.
REST API	Provides a set of HTTP endpoints to integrate video generation into your applications. It requires you to handle API requests and authentication.	Ideal for developers who need to integrate video generation into applications written in any language that supports HTTP requests.
Vertex AI SDK for Python	A Python library that simplifies interactions with the Vertex AI API, making it easier to automate video generation workflows.	Recommended for Python developers who want to programmatically generate videos or integrate this functionality into their Python-based applications and machine learning workflows.

The following examples show how you can use text prompts to generate videos:

Console

In the Google Cloud console, go to the Vertex AI > Media Studio page.

Media Studio
Click Veo. The Veo Media Studio page is displayed.
Optional: In the Settings pane, configure the following settings:
- Model: choose a model from the available options.
  
  For more information about available models, see Veo models.
- Number of results: adjust the slider or enter a value. The maximum value depends on the model that you selected.
- Video length: select a length between 5 seconds and 8 seconds.
- Output directory: click Browse to create or select a Cloud Storage bucket to store output files.
Optional: In the Safety section, select one of the following Person generation settings:
- Allow (Adults only): default value. Generate adult people or faces only. Don't generate youth or children people or faces.
- Don't allow: don't generate people or faces.
Optional: In the Advanced options section, enter a Seed value. Providing a seed value ensures that you get the same output with the same text prompt.
In the Write your prompt box, enter your text prompt that describes the videos to generate.
Click Generate.

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

For more information about the Veo API, see the Veo on Vertex AI API.

Use the following command to send a video generation request. This request begins a long-running operation and stores output to a Cloud Storage bucket you specify.

Before using any of the request data, make the following replacements:
- PROJECT_ID: A string representing your Google Cloud project ID.
- MODEL_ID: A string respresenting the model ID to use. The following are accepted values:
  - "veo-2.0-generate-001"
  - "veo-3.0-generate-001"
  - "veo-3.0-fast-generate-001"
  - "veo-3.0-generate-preview" (Preview)
  - "veo-3.0-fast-generate-preview" (Preview)
- TEXT_PROMPT: The text prompt used to guide video generation.
- OUTPUT_STORAGE_URI: Optional: A string representing the Cloud Storage bucket to store the output videos. If not provided, video bytes are returned in the response. For example: "gs://video-bucket/output/".
- RESPONSE_COUNT: The number of video files to generate. The accepted range of values is 1-4.
- DURATION: An integer representing the length of the generated video files. The following are the accepted values for each model:
  - Veo 2 models: 5-8
  - Veo 3 models: 8
- Additional optional parameters
  
  Use the following optional variables depending on your use case. Add some or all of the following parameters in the "parameters": {} object.
```
"parameters": {
  "aspectRatio": "ASPECT_RATIO",
  "negativePrompt": "NEGATIVE_PROMPT",
  "personGeneration": "PERSON_SAFETY_SETTING",
  // "resolution": RESOLUTION, // Veo 3 models only
  "sampleCount": RESPONSE_COUNT,
  "seed": SEED_NUMBER
}
```
  - ASPECT_RATIO: string. Optional. Defines the aspect ratio of the generated videos. Values: 16:9 (default, landscape) or 9:16 (portrait).
  - NEGATIVE_PROMPT: string. Optional. A text string that describes what you want to discourage the model from generating.
  - PERSON_SAFETY_SETTING: string. Optional. The safety setting that controls whether people or face generation is allowed. Values:
    - allow_adult (default value): Allow generation of adults only.
    - disallow: Disallows inclusion of people or faces in images.
  - RESOLUTION: string. Optional. Veo 3 models only. The resolution of the generated video. Values: 720p (default) or 1080p.
  - RESPONSE_COUNT: int. Optional. The number of output images requested. Values: 1-4.
  - SEED_NUMBER: uint32. Optional. A number to make generated videos deterministic. Specifying a seed number with your request without changing other parameters guides the model to produce the same videos. Values: 0 - 4294967295.
HTTP method and URL:
```
POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning
```
Request JSON body:
```
{
  "instances": [
    {
      "prompt": "TEXT_PROMPT"
    }
  ],
  "parameters": {
    "storageUri": "OUTPUT_STORAGE_URI",
    "sampleCount": "RESPONSE_COUNT"
  }
}
```
To send your request, choose one of these options:
curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning"
PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:
$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning" | Select-Object -Expand Content
This request returns a full operation name with a unique operation ID. Use this full operation name to poll that status of the video generation request.
```
{
  "name": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8"
}
```

Optional: Check the status of the video generation long-running operation.

Before using any of the request data, make the following replacements:

PROJECT_ID: Your Google Cloud project ID.
MODEL_ID: The model ID to use.
OPERATION_ID: The unique operation ID returned in the original generate video request.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation

Request JSON body:

{
  "operationName": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID"
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation" | Select-Object -Expand Content

This request returns information about the operation, including if the operation is still running or is done.

Response

{
  "name": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID",
  "done": true,
  "response": {
    "raiMediaFilteredCount": 0,
    "@type": "type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse",
    "videos": [
      {
        "gcsUri":"gs://BUCKET_NAME/TIMESTAMPED_FOLDER/sample_0.mp4",
        "mimeType": "video/mp4"
      }
    ]
  }
}

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

import time
from google import genai
from google.genai.types import GenerateVideosConfig

client = genai.Client()

# TODO(developer): Update and un-comment below line
# output_gcs_uri = "gs://your-bucket/your-prefix"

operation = client.models.generate_videos(
    model="veo-3.0-generate-001",
    prompt="a cat reading a book",
    config=GenerateVideosConfig(
        aspect_ratio="16:9",
        output_gcs_uri=output_gcs_uri,
    ),
)

while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)
    print(operation)

if operation.response:
    print(operation.result.generated_videos[0].video.uri)

# Example response:
# gs://your-bucket/your-prefix

Generate videos using text prompts Stay organized with collections Save and categorize content based on your preferences.

Before you begin

Console

REST

Generate videos from text

Console

REST

curl

PowerShell

curl

PowerShell

Response

Python

Install

What's next

Generate videos using text prompts