Generate videos from the first and last frames

Console

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

   Media Studio

2. Click Video. The Video Media Studio page is displayed.

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 the generated files.

4. In the Write your prompt prompt box, click uploadUpload. In the displayed dialog, select an image for the first frame.

   Add ending frame (Optional) is displayed next to the uploaded image.

5. Optional: Add an end frame by clicking Add ending frame. In the displayed dialog, select an image for the last frame.

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

7. 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, Image

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 hand reaches in and places a glass of milk next to the plate of cookies",
    image=Image(
        gcs_uri="gs://cloud-samples-data/generative-ai/image/cookies.png",
        mime_type="image/png",
    ),
    config=GenerateVideosConfig(
        aspect_ratio="16:9",
        last_frame=Image(
            gcs_uri="gs://cloud-samples-data/generative-ai/image/cookies-milk.png",
            mime_type="image/png",
        ),
        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_FIRST_FRAME_IMAGE: The Cloud Storage path to the first frame's image.
   • IMAGE_MIME_TYPE: The MIME type of the input image. Only one of the following:
     • image/jpeg
     • image/png
   • PATH_TO_LAST_FRAME_IMAGE: The Cloud Storage path to the last frame's image.
   • 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: 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-001:predictLongRunning

   Request JSON body:

   {
     "instances": [
       {
         "prompt": "TEXT_PROMPT",
          "image": {
            "gcsUri": "PATH_TO_FIRST_FRAME_IMAGE,
            "mimeType": "IMAGE_MIME_TYPE"
          },
          "lastFrame:" {
            "gcsUri": "PATH_TO_LAST_FRAME_IMAGE,
            "mimeType": "IMAGE_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-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"
         }
       ]
     }
   }