Veo를 사용하여 동영상 생성

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

client = genai.Client()

operation = client.models.generate_videos(
    model="veo-3.0-generate-preview",
    prompt="Panning wide shot of a purring kitten sleeping in the sunshine",
    config=types.GenerateVideosConfig(
        person_generation="allow_all",  # "allow_adult" and "dont_allow" for Veo 2 only
        aspect_ratio="16:9",  # "16:9", and "9:16" for Veo 2 only
    ),
)

while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

for n, generated_video in enumerate(operation.response.generated_videos):
    client.files.download(file=generated_video.video)
    generated_video.video.save(f"video{n}.mp4")

import { GoogleGenAI } from "@google/genai";
import { createWriteStream } from "fs";
import { Readable } from "stream";

const ai = new GoogleGenAI({});

async function main() {
  let operation = await ai.models.generateVideos({
    model: "veo-3.0-generate-preview",
    prompt: "Panning wide shot of a purring kitten sleeping in the sunshine",
    config: {
      personGeneration: "allow_all",
      aspectRatio: "16:9",
    },
  });

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

  operation.response?.generatedVideos?.forEach(async (generatedVideo, n) => {
    const resp = await fetch(`${generatedVideo.video?.uri}&key=GEMINI_API_KEY`); // append your API key
    const writer = createWriteStream(`video${n}.mp4`);
    Readable.fromWeb(resp.body).pipe(writer);
  });
}

main();

package main

import (
  "context"
  "fmt"
  "os"
  "time"
  "google.golang.org/genai"
)

func main() {

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

  videoConfig := &genai.GenerateVideosConfig{
      AspectRatio:      "16:9",
      PersonGeneration: "allow_all",
  }

  operation, _ := client.Models.GenerateVideos(
      ctx,
      "veo-3.0-generate-preview",
      "Panning wide shot of a purring kitten sleeping in the sunshine",
      nil,
      videoConfig,
  )

  for !operation.Done {
      time.Sleep(20 * time.Second)
      operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
  }

  for n, video := range operation.Response.GeneratedVideos {
      client.Files.Download(ctx, video.Video, nil)
      fname := fmt.Sprintf("video_%d.mp4", n)
      _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
  }
}

# Use curl to send a POST request to the predictLongRunning endpoint.
# The request body includes the prompt for video generation.
curl "${BASE_URL}/models/veo-3.0-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
        "prompt": "Panning wide shot of a purring kitten sleeping in the sunshine"
      }
    ],
    "parameters": {
      "aspectRatio": "16:9",
      "personGeneration": "allow_all",
    }
  }' | tee result.json | jq .name | sed 's/"//g' > op_name

# Obtain operation name to download video.
op_name=$(cat op_name)

# Check against status of operation.
while true; do
  is_done=$(curl -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${op_name}" | tee op_check.json | jq .done)

  if [ "${is_done}" = "true" ]; then
    cat op_check.json
    echo "** Attach API_KEY to download video, or examine error message."
    break
  fi

  echo "** Video ${op_name} has not downloaded yet!  Check again after 5 seconds..."

  # Wait for 5 seoncds to check again.
  sleep 5

done

prompt="Panning wide shot of a calico kitten sleeping in the sunshine",

imagen = client.models.generate_images(
    model="imagen-3.0-generate-002",
    prompt=prompt,
    config=types.GenerateImagesConfig(
      aspect_ratio="16:9",
      number_of_images=1
    )
)

imagen.generated_images[0].image

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

const ai = new GoogleGenAI({});
const response = await ai.models.generateImages({
  model: "imagen-3.0-generate-002",
  prompt: "Panning wide shot of a calico kitten sleeping in the sunshine",
  config: {
    numberOfImages: 1,
  },
});

// you'll pass response.generatedImages[0].image.imageBytes to Veo

package main

import (
    "context"
    "fmt"
    "os"
    "time"
    "google.golang.org/genai"
)

func main() {

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

    config := &genai.GenerateImagesConfig{
        AspectRatio:    "16:9",
        NumberOfImages: 1,
    }

    response, _ := client.Models.GenerateImages(
        ctx,
        "imagen-3.0-generate-002",
        "Panning wide shot of a calico kitten sleeping in the sunshine",
        config,
    )

    // you'll pass response.GeneratedImages[0].Image to Veo
}

operation = client.models.generate_videos(
    model="veo-2.0-generate-001",
    prompt=prompt,
    image = imagen.generated_images[0].image,
    config=types.GenerateVideosConfig(
      person_generation="dont_allow",  # "dont_allow" or "allow_adult"
      aspect_ratio="16:9",  # "16:9" or "9:16"
      number_of_videos=2
    ),
)

# Wait for videos to generate
 while not operation.done:
  time.sleep(20)
  operation = client.operations.get(operation)

for n, video in enumerate(operation.response.generated_videos):
    fname = f'with_image_input{n}.mp4'
    print(fname)
    client.files.download(file=video.video)
    video.video.save(fname)

import { GoogleGenAI } from "@google/genai";
import { createWriteStream } from "fs";
import { Readable } from "stream";

const ai = new GoogleGenAI({});

async function main() {
  // get image bytes from Imagen, as shown above

  let operation = await ai.models.generateVideos({
    model: "veo-2.0-generate-001",
    prompt: "Panning wide shot of a calico kitten sleeping in the sunshine",
    image: {
      imageBytes: response.generatedImages[0].image.imageBytes, // response from Imagen
      mimeType: "image/png",
    },
    config: {
      aspectRatio: "16:9",
      numberOfVideos: 2,
    },
  });

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

  operation.response?.generatedVideos?.forEach(async (generatedVideo, n) => {
    const resp = await fetch(
      `${generatedVideo.video?.uri}&key=GEMINI_API_KEY`, // append your API key
    );
    const writer = createWriteStream(`video${n}.mp4`);
    Readable.fromWeb(resp.body).pipe(writer);
  });
}

main();

    image := response.GeneratedImages[0].Image

    videoConfig := &genai.GenerateVideosConfig{
      AspectRatio:    "16:9",
      NumberOfVideos: 2,
    }

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-2.0-generate-001",
        "A dramatic scene based on the input image",
        image,
        videoConfig,
    )

    for !operation.Done {
        time.Sleep(20 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    for n, video := range operation.Response.GeneratedVideos {
        client.Files.Download(ctx, video.Video, nil)
        fname := fmt.Sprintf("video_with_image_input_%d.mp4", n)
        _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    }