Extend a Veo video

Console

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

   Media Studio

2. Click Video to open the Video Media Studio page.

3. In the Settings pane, configure the following settings:

   • Model: Select Veo 2 veo-2.0-generate-001.

   • Aspect ratio: Choose either 16:9 or 9:16.

   • Number of results: Adjust the slider or enter a value between 1 and 4.

   • 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.

4. In the Write your prompt box, enter your text prompt that describes the videos to generate.

5. Click send Generate.

6. Hover over the video you want to extend then click auto_fix_high class="material-icons">auto_fix_high AI actions > Extend video.

7. In the Write your prompt box, enter your text prompt that describes the videos to generate.

8. Click send Generate.

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, Video

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-2.0-generate-001",
    prompt="a butterfly flies in and lands on the flower",
    video=Video(
        uri="gs://cloud-samples-data/generative-ai/video/flower.mp4",
    ),
    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

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.

1. 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: Your Google Cloud project ID.
   • TEXT_PROMPT: The text prompt used to guide video generation.
   • PATH_TO_VIDEO: The Cloud Storage path to the Veo video that you're extending.
   • OUTPUT_STORAGE_URI: Optional: 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 you want to generate. Accepted integer values: 1-4.
   • DURATION: The length of video files that you want to generate. Accepted integer values are 5-8.

   • 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/veo-2.0-generate-001:predictLongRunning

   Request JSON body:

   {
     "instances": [
       {
         "prompt": "TEXT_PROMPT",
          "video": {
            "gcsUri": "PATH_TO_VIDEO",
            "mimeType": "video/mp4"
          }
       }
     ],
     "parameters": {
       "storageUri": "OUTPUT_STORAGE_URI",
       "sampleCount": RESPONSE_COUNT
     }
   }
   

   To send your request, choose one of these options:

   curl

   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/veo-2.0-generate-001:predictLongRunning"

   PowerShell

   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/veo-2.0-generate-001: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/veo-2.0-generate-001/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8"
   }
   

2. 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

   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

   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"
         }
       ]
     }
   }