uv pip install google-genai

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

# Initialize client
client = genai.Client(
    vertexai=True,
    project=os.getenv("GOOGLE_CLOUD_PROJECT"),  # Set via environment variable
    location="us-central1"
)

# Generate video from text
operation = client.models.generate_videos(
    model='veo-3.1-generate-preview',
    prompt='A neon hologram of a cat driving at top speed',
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=5,
        # enhance_prompt defaults to True and cannot be disabled in Veo 3.1
    ),
)

# Poll until complete (typically 2-5 minutes)
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

# Check for errors before accessing response
if operation.error:
    raise Exception(f"Video generation failed: {operation.error}")

# Save the generated video
video = operation.response.generated_videos[0].video
with open('output.mp4', 'wb') as f:
    f.write(video.video_bytes)

Medium shot, a tired corporate worker, rubbing his temples in exhaustion,
in front of a bulky 1980s computer in a cluttered office late at night.
The scene is lit by the harsh fluorescent overhead lights and the green
glow of the monochrome monitor. Retro aesthetic, shot as if on 1980s
color film, slightly grainy.

operation = client.models.generate_videos(
    model='veo-3.1-generate-preview',
    prompt='Your detailed prompt here',
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=6,
        aspect_ratio='16:9',
        resolution='1080p',
    ),
)

# Poll and save
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

# Check for errors
if operation.error:
    raise Exception(f"Video generation failed: {operation.error}")

video = operation.response.generated_videos[0].video
with open('output.mp4', 'wb') as f:
    f.write(video.video_bytes)

from PIL import Image as PILImage

# Load image with proper MIME type
with open('path/to/image.jpg', 'rb') as f:
    image_data = f.read()

image = types.Image(
    image_bytes=image_data,
    mime_type='image/jpeg'  # or 'image/png' for PNG files
)

# Detect aspect ratio from image
pil_image = PILImage.open('path/to/image.jpg')
width, height = pil_image.size
aspect_ratio = '16:9' if width > height else '9:16'

operation = client.models.generate_videos(
    model='veo-3.1-generate-preview',
    prompt='Slow dolly shot moving closer, cinematic lighting',
    image=image,
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=5,
        aspect_ratio=aspect_ratio,
        resolution='1080p',
    ),
)

# Poll and save
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

# Check for errors
if operation.error:
    raise Exception(f"Video generation failed: {operation.error}")

video = operation.response.generated_videos[0].video
with open('output.mp4', 'wb') as f:
    f.write(video.video_bytes)

video_input = types.Video(
    uri="gs://bucket-name/video.mp4",  # GCS URI for Vertex AI
)

operation = client.models.generate_videos(
    model='veo-3.1-generate-preview',
    prompt='Transform into cyberpunk style with neon lights',
    video=video_input,
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=5,
    ),
)

# Poll and save
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

# Check for errors
if operation.error:
    raise Exception(f"Video generation failed: {operation.error}")

video = operation.response.generated_videos[0].video
with open('output.mp4', 'wb') as f:
    f.write(video.video_bytes)

A woman says, "We have to leave now."

SFX: thunder cracks in the distance

Ambient noise: the quiet hum of a starship bridge