Generate videos with Veo on Vertex AI from an image

You can use Veo on Vertex AI to generate new videos from an image and text prompt. Supported interfaces include the Google Cloud console and the Vertex AI API.

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

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.

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.

Enable the API

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

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.

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.

Generate videos from an image

Sample input	Sample output
Input image^1 Text prompt: the elephant moves around naturally

¹ Image generated using Imagen on Vertex AI from the prompt: A Crochet elephant in intricate patterns walking on the savanna

You can generate novel videos using only an image as an input, or and image and descriptive text as the inputs. The following samples show you basic instructions to generate videos from image and text.

Console

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

   Media Studio

2. Click Video.

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

   • Model: choose a model from the available options.
   • 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. Optional: In the Safety section, select one of the following Person generation settings:

   • Allow (Adults only): default value. Generate adult people or faces only. Don't generate youth or children people or faces.

   • Don't allow: don't generate people or faces.

5. Optional: In the Advanced options section, enter a Seed value for randomizing video generation.

6. In the Write your prompt prompt box, click Upload.

7. Choose a local image to upload and click Select.

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

9. Click Generate.

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: A string representing your Google Cloud project ID.
   • MODEL_ID: A string respresenting the model ID to use. The following are accepted values:
     • veo-2.0-generate-001 (GA)
     • veo-3.0-generate-preview (Preview)
   • TEXT_PROMPT: The text prompt used to guide video generation.
   • INPUT_IMAGE: A base64-encoded string that represents the input image. For best quality, we recommend that the input image's resolution be 720p (1280 x 720 pixels) or higher, and have an aspect ratio of either 16:9 or 9:16. Images of other aspect ratios or sizes may be resized or centrally cropped when the image is uploaded.
   • MIME_TYPE: A string representing the MIME type of the input image. Only the images of the following MIME types are supported:
     • "image/jpeg"
     • "image/png"
   • OUTPUT_STORAGE_URI: Optional: A string representing 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 to generate. The accepted range of values is 1-4.
   • DURATION: An integer representing the length of the generated video files. The following are the accepted values for each model:
     • Veo 2 models: 5-8
     • Veo 3 models: 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/MODEL_ID:predictLongRunning

   Request JSON body:

   {
     "instances": [
       {
         "prompt": "TEXT_PROMPT",
         "image": {
           "bytesBase64Encoded": "INPUT_IMAGE",
           "mimeType": "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/MODEL_ID: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/MODEL_ID: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/MODEL_ID/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"
         }
       ]
     }
   }
   

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-3.0-generate-preview",
    prompt="Extreme close-up of a cluster of vibrant wildflowers swaying gently in a sun-drenched meadow.",
    image=Image(
        gcs_uri="gs://cloud-samples-data/generative-ai/image/flowers.png",
        mime_type="image/png",
    ),
    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

What's next

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