Generate videos from an image

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 uploadUpload.

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 send 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: string. Optional. Defines the aspect ratio of the generated videos. Values: 16:9 (default, landscape) or 9:16 (portrait).
     • NEGATIVE_PROMPT: string. Optional. A text string that describes what you want to discourage the model from generating.
     • PERSON_SAFETY_SETTING: string. Optional. The safety setting that controls whether people or face generation is allowed. Values:
       • allow_adult (default value): Allow generation of adults only.
       • disallow: Disallows inclusion of people or faces in images.
     • RESOLUTION: string. Optional. Veo 3 models only. The resolution of the generated video. Values: 720p (default) or 1080p.
     • RESPONSE_COUNT: int. Optional. The number of output images requested. Values: 1-4.
     • SEED_NUMBER: uint32. Optional. A number to make generated videos deterministic. Specifying a seed number with your request without changing other parameters guides the model to produce the same videos. Values: 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

   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

   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

   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"
         }
       ]
     }
   }
   

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