Remove objects from Veo videos

Console

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

   Media Studio

2. Click Video.

3. In the Task menu, select Video inpaint (insert).

4. From the Model menu, select a model from the displayed options.

5. In the Input video section, click Add.

6. In the Prompt box, 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 1 and 4.

   • Mask behavior: Select an option from the displayed options.

   • Output directory: click Browse to 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 Google 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_ENTERPRISE=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(
        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.REMOVE,
        ),
        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

For more information about the Veo API, see the following:

• Method: endpoints.predict
• VideoGenerationModelInstance
• VideoGenerationModelParams
• VideoGenerationModelResult

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": "remove"
      },
      "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 -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"

$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

{
  "name": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/veo-2.0-generate-001/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8"
}

1. 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"
         }
       ]
     }
   }