You can use Veo on Vertex AI to generate new videos from an image and 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.
1 Image generated using Imagen on Vertex AI from the prompt: A Crochet
elephant in intricate patterns walking on the savanna You can generate novel videos using only an image as an input, or and image and
descriptive text as the inputs. The following samples show you basic
instructions to generate videos from image and text.
In the Google Cloud console, go to the Vertex AI Studio > Media
Studio page. Click Video. Optional: In the Settings pane, configure the following settings: 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 for
randomizing video generation. In the Write your prompt prompt box, click Choose a local image to upload and click Select. 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
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 an image
Sample input
Sample output
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
(GA)veo-3.0-generate-preview
(Preview)TEXT_PROMPT
: The
text prompt used to guide video generation.
INPUT_IMAGE
: A
base64-encoded string that represents the input image. For best quality, we
recommend that the input image's resolution be 720p (1280 x 720 pixels) or
higher, and have an aspect ratio of either 16:9 or 9:16. Images of other
aspect ratios or sizes may be resized or centrally cropped when the image is
uploaded.
MIME_TYPE
: A string
representing the MIME type of the input image. Only the images of the
following MIME types are supported:
"image/jpeg"
"image/png"
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
}
16:9
(default, landscape) or 9:16
(portrait).
allow_adult
(default value): Allow generation of adults only.disallow
: Disallows inclusion of people or faces in images.720p
(default) or 1080p
.
1
-4
.
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",
"image": {
"bytesBase64Encoded": "INPUT_IMAGE",
"mimeType": "MIME_TYPE"
}
}
],
"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 text
- Learn more about prompts
- Understand responsible AI and usage guidelines for Veo on Vertex AI