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    Insert objects into Veo videos

    You can use Veo on Colab Enterprise to insert objects into videos by providing a mask and an image object, then providing a prompt to the model that includes a description of the output that you want.

    The following models support inserting objects into videos:

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

    Try Veo in a Colab

    Before you begin

    1. 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.

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

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    3. Enable the Agent Platform API.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the API

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

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    5. Enable the Agent Platform API.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the API

    6. 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.

      Python

      To use the Python samples on this page in a local development environment, install and initialize the gcloud CLI, and then set up Application Default Credentials with your user credentials.

      1. Install the Google Cloud CLI.

      2. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

      3. If you're using a local shell, then create local authentication credentials for your user account:

        gcloud  
auth  
application-default  
login

        You don't need to do this if you're using Cloud Shell.

        If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

      For more information, see Set up ADC for a local development environment in the Google Cloud authentication documentation.

      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.

        Install the Google Cloud CLI.

        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.

    Insert an object into a video

    Console

    1. In the Google Cloud console, go to the Agent Platform > Media Studiopage.

      Media Studio

    2. Click Video.

    3. In the Taskmenu, select Video inpaint (insert).

    4. From the Modelmenu, select a model from the displayed options.

    5. In the Input videosection, click Add.

    6. In the Promptbox, enter a text prompt that describes the videos to generate.

    7. Optional: Adjust the following Parameters:

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

      • Output directory: click Browseto create or select a Cloud Storage bucket to store the generated files.

    8. Click Run.

    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 Agent Platform:

     # 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 
 GenerateVideosSource 
 , 
 GenerateVideosConfig 
 , 
 Image 
 , 
 Video 
 , 
 VideoGenerationMask 
 , 
 VideoGenerationMaskMode 
 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-preview" 
 , 
 source 
 = 
 GenerateVideosSource 
 ( 
 prompt 
 = 
 "a sheep" 
 , 
 video 
 = 
 Video 
 ( 
 uri 
 = 
 "gs://cloud-samples-data/generative-ai/video/truck.mp4" 
 , 
 mime_type 
 = 
 "video/mp4" 
 ) 
 ), 
 config 
 = 
 GenerateVideosConfig 
 ( 
 mask 
 = 
 VideoGenerationMask 
 ( 
 image 
 = 
 Image 
 ( 
 gcs_uri 
 = 
 "gs://cloud-samples-data/generative-ai/image/truck-inpainting-dynamic-mask.png" 
 , 
 mime_type 
 = 
 "image/png" 
 , 
 ), 
 mask_mode 
 = 
 VideoGenerationMaskMode 
 . 
 INSERT 
 , 
 ), 
 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 Colab Enterprise API reference documentation .

    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.
      • MASK_STORAGE_URI The Cloud Storage bucket URI path to the mask object.

      • MASK_MIME_TYPE The MIME type of the image mask. Only one of the following:

        • image/png
        • image/jpeg
        • image/webp
      • VIDEO_INPUT_STORAGE_URI The Cloud Storage bucket URI path video input object.

      • VIDEO_MIME_TYPE The MIME type of the video object. Only one of the following:

        • video/mov
        • video/mpeg
        • video/mp4
        • video/mpg
        • video/avi
        • video/wmv
        • video/mpegps
        • video/flv
      • OUTPUT_STORAGE_URI : Optional: The Cloud Storage bucket to store the output videos. If not provided, a Base64-bytes encoded video is 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.

      • 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 : 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

          The default value is "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.

          The default value is "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"
          • "4k" (Veo 3.1 Preview models only)

          The default value is "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 .

      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-preview:predictLongRunning

      Request JSON body:

      {
  "instances": [
    {
      "prompt": " TEXT_PROMPT 
",
      // The following fields can be repeated for up to three total
      // images.
      "mask": {
        "gcsUri": " MASK_STORAGE_URI 
",
        "mimeType": " MASK_MIME_TYPE 
",
        "maskMode": "insert"
      },
      "video": {
        "gcsUri": " VIDEO_INPUT_STORAGE_URI 
",
        "mimeType": " VIDEO_MIME_TYPE 
"
      }
    }
  ],
  "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-preview: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-preview: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.

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