Membuat video dengan Veo 3.1 di Gemini API

Untuk mempelajari pemahaman video, lihat panduan Pemahaman video.

Veo 3.1 adalah model untuk membuat video berdurasi 8 detik (720p, 1080p, atau 4k) dengan audio yang dibuat secara native. Anda dapat mengakses model ini secara terprogram menggunakan Gemini API. Untuk mempelajari lebih lanjut varian model Veo yang tersedia, lihat bagian Versi Model.

Veo 3.1 unggul dalam berbagai gaya visual dan sinematik serta memperkenalkan beberapa kemampuan baru:

  • Video potret: Pilih antara video lanskap (16:9) dan potret (9:16).
  • Ekstensi video: Memperpanjang durasi video yang sebelumnya dibuat menggunakan Veo.
  • Pembuatan spesifik per frame: Buat video dengan menentukan frame pertama dan terakhir.
  • Arahan berbasis gambar: Gunakan hingga tiga gambar referensi untuk memandu konten video yang dibuat.

Untuk mengetahui informasi selengkapnya tentang cara menulis perintah teks yang efektif untuk pembuatan video, lihat panduan perintah Veo.

Pembuatan video dari teks

Contoh berikut menunjukkan cara membuat video dengan dialog, realisme sinematik, atau animasi kreatif:

Dialog & efek suara

Python

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

client = genai.Client()

prompt = """A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.
A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'"""

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the generated video.
generated_video = operation.response.generated_videos[0]
client.files.download(file=generated_video.video)
generated_video.video.save("dialogue_example.mp4")
print("Generated video saved to dialogue_example.mp4")

JavaScript

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

const ai = new GoogleGenAI({});

const prompt = `A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.
A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'`;

let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    prompt: prompt,
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the generated video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "dialogue_example.mp4",
});
console.log(`Generated video saved to dialogue_example.mp4`);

Go

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)
    }

    prompt := `A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.
    A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'`

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
        nil,
        nil,
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
    log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the generated video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "dialogue_example.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateVideosOperation;
import com.google.genai.types.Video;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

class GenerateVideoFromText {
  public static void main(String[] args) throws Exception {
    Client client = new Client();

    String prompt = "A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.\n" +
"A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'";

    GenerateVideosOperation operation =
        client.models.generateVideos("veo-3.1-generate-preview", prompt, null, null);

    // Poll the operation status until the video is ready.
    while (!operation.done().isPresent() || !operation.done().get()) {
      System.out.println("Waiting for video generation to complete...");
      Thread.sleep(10000);
      operation = client.operations.getVideosOperation(operation, null);
    }

    // Download the generated video.
    Video video = operation.response().get().generatedVideos().get().get(0).video().get();
    Path path = Paths.get("dialogue_example.mp4");
    client.files.download(video, path.toString(), null);
    if (video.videoBytes().isPresent()) {
      Files.write(path, video.videoBytes().get());
      System.out.println("Generated video saved to dialogue_example.mp4");
    }
  }
}

REST

# Note: This script uses jq to parse the JSON response.
# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
        "prompt": "A close up of two people staring at a cryptic drawing on a wall, torchlight flickering. A man murmurs, \"This must be it. That'\''s the secret code.\" The woman looks at him and whispering excitedly, \"What did you find?\""
      }
    ]
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o dialogue_example.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 5 seconds before checking again.
  sleep 10
done

Realisme sinematik

Python

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

client = genai.Client()

prompt = """Drone shot following a classic red convertible driven by a man along a winding coastal road at sunset, waves crashing against the rocks below.
The convertible accelerates fast and the engine roars loudly."""

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the generated video.
generated_video = operation.response.generated_videos[0]
client.files.download(file=generated_video.video)
generated_video.video.save("realism_example.mp4")
print("Generated video saved to realism_example.mp4")

JavaScript

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

const ai = new GoogleGenAI({});

const prompt = `Drone shot following a classic red convertible driven by a man along a winding coastal road at sunset, waves crashing against the rocks below.
The convertible accelerates fast and the engine roars loudly.`;

let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    prompt: prompt,
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the generated video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,