veo 3 1 · veo Reference

import time
from google import genai
from google.genai import types

client = genai.Client()

# Step 1: Generate starting keyframe image
img_response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents="A panoramic shot of a calico kitten sleeping in a sunlit garden surrounded by lavender.",
    config={"response_modalities": ["IMAGE"]},
)
starting_image = img_response.parts[0].as_image()

# Step 2: Animate the keyframe with Veo 3.1
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="The calico kitten wakes up, stretches its paws lazily, and yawns in the warm sunlight, gentle birdsong in the garden.",
    image=starting_image,
    config=types.GenerateVideosConfig(
        aspect_ratio="16:9",
        resolution="1080p",
        duration_seconds=8,
    ),
)

while not operation.done:
    print("Waiting for Veo video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

video = operation.response.generated_videos[0]
client.files.download(file=video.video)
video.video.save("calico_kitten.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

// 1. Reference-Guided Video Generation (up to 3 asset references)
let operation = await ai.models.generateVideos({
  model: "veo-3.1-generate-preview",
  prompt:
    "The woman walks with serene confidence through shallow turquoise water wearing the flamingo dress and heart-shaped sunglasses, gentle ocean waves lapping.",
  config: {
    aspectRatio: "16:9",
    durationSeconds: 8,
    referenceImages: [
      { image: dressImage, referenceType: "asset" },
      { image: glassesImage, referenceType: "asset" },
      { image: womanImage, referenceType: "asset" },
    ],
  },
});

while (!operation.done) {
  await new Promise((resolve) => setTimeout(resolve, 10000));
  operation = await ai.operations.getVideosOperation({ operation });
}

await ai.files.download({
  file: operation.response!.generatedVideos![0].video!,
  downloadPath: "flamingo_fashion.mp4",
});

package main

import (
	"context"
	"log"
	"os"
	"time"

	"google.golang.org/genai"
)

func main() {
	ctx := context.Background()
	client, err := genai.NewClient(ctx, nil)
	if err != nil {
		log.Fatal(err)
	}

	// Step 1: Generate 720p base video (required for extension)
	op, err := client.Models.GenerateVideos(
		ctx,
		"veo-3.1-generate-preview",
		"An origami butterfly flaps its wings and flies out through French doors into a sunlit garden.",
		nil,
		&genai.GenerateVideosConfig{
			AspectRatio: "16:9",
			Resolution:  "720p",
		},
	)
	if err != nil {
		log.Fatal(err)
	}

	for !op.Done {
		time.Sleep(10 * time.Second)
		op, err = client.Operations.GetVideosOperation(ctx, op, nil)
		if err != nil {
			log.Fatal(err)
		}
	}

	baseVideo := op.Response.GeneratedVideos[0]
	if _, err := client.Files.Download(ctx, baseVideo.Video, nil); err != nil {
		log.Fatal(err)
	}
	if err := os.WriteFile("butterfly_base.mp4", baseVideo.Video.VideoBytes, 0644); err != nil {
		log.Fatal(err)
	}

	// Step 2: Extend the generated video by +7 seconds at 720p
	extOp, err := client.Models.GenerateVideos(
		ctx,
		"veo-3.1-generate-preview",
		"Track the origami butterfly as it lands on an orange flower while a white puppy runs up.",
		nil,
		&genai.GenerateVideosConfig{
			NumberOfVideos: 1,
			Resolution:     "720p",
		},
	)
	_ = extOp
}