Insert objects into Veo videos

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

You can use Veo on Vertex AI 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.

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

Note: The Vertex AI API is supported during Preview. Supported interfaces include the Google Cloud console and the Vertex AI API.

Try Veo in a Colab

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.

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 (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Enable the Vertex AI 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

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 (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Enable the Vertex AI 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

1. 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.

   Install the Google Cloud CLI. After installation, initialize the Google Cloud CLI by running the following command:

   gcloud init

   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

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 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"

       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

   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/veo-2.0-generate-preview: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/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

   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"
         }
       ]
     }
   }
   

What's next

• Generate videos from text
• Learn more about prompts
• Understand responsible AI and usage guidelines for Veo on Vertex AI