You can use Veo on Vertex AI to generate new videos from a text prompt. Supported
interfaces include the Google Cloud console and the Vertex AI API. For more information about writing effective text prompts for video generation,
see the Veo prompt
guide. In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
Enable the Vertex AI API.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
Enable the Vertex AI API.
Set up authentication for your environment. Select the tab for how you plan to use the samples on this page:
When you use the Google Cloud console to access Google Cloud services and
APIs, you don't need to set up authentication.
To use the REST API samples on this page in a local development environment, you use the
credentials you provide to the gcloud CLI.
Install the Google Cloud CLI.
After installation,
initialize the Google Cloud CLI by running the following command:
If you're using an external identity provider (IdP), you must first
sign in to the gcloud CLI with your federated identity.
For more information, see
Authenticate for using REST
in the Google Cloud authentication documentation.
The following examples show how you can use text prompts to generate videos: In the Google Cloud console, go to the Vertex AI > Media
Studio page. 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
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:
Additional optional parameters Use the following optional variables depending on your use
case. Add some or all of the following parameters in the The default value is The default value is The default value is
HTTP method and URL:
Request JSON body:
To send your request, choose one of these options:
Save the request body in a file named
Save the request body in a file named Optional: Check the status of the video generation long-running
operation.
Before using any of the request data,
make the following replacements:
HTTP method and URL:
Request JSON body:
To send your request, choose one of these options:
Save the request body in a file named
Save the request body in a file named
To learn more, see the
SDK reference documentation.
Set environment variables to use the Gen AI SDK with Vertex AI:
Before you begin
Console
REST
gcloud init
Generate videos from text
Console
REST
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:
5
-8
8
"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
:
Optional: A string value that describes the aspect ratio of the generated
videos. You can use the following values:
"16:9"
for landscape"9:16"
for portrait"16:9"
NEGATIVE_PROMPT
: Optional: A string
value that describes content that you want to prevent the model from
generating.
PERSON_SAFETY_SETTING
:
Optional: A string value that controls the safety setting for generating
people or face generation. You can use the following values:
"allow_adult"
: Only allow generation of adult people and
faces.
"disallow"
: Doesn't generate people or faces.
"allow_adult"
.RESOLUTION
:
Optional: A string value that controls the resolution of the generated
video. Supported by Veo 3 models only. You can use the following
values:
"720p"
"1080p"
"720p"
.RESPONSE_COUNT
:
Optional. An integer value that describes the number of videos to generate.
The accepted range of values is 1
-4
.
SEED_NUMBER
:
Optional. An uint32 value that the model uses to generate deterministic
videos. Specifying a seed number with your request without changing other
parameters guides the model to produce the same videos. The accepted range
of values is 0
-4294967295
.
POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning
{
"instances": [
{
"prompt": "TEXT_PROMPT"
}
],
"parameters": {
"storageUri": "OUTPUT_STORAGE_URI",
"sampleCount": "RESPONSE_COUNT"
}
}
curl
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
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
{
"name": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8"
}
POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation
{
"operationName": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID"
}
curl
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
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 ContentPython
Install
pip install --upgrade google-genai
# 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
What's next
- Generate videos from an image
- Learn more about prompts
- Understand responsible AI and usage guidelines for Veo on Vertex AI