veo

# 1. Verify or install gemini-api CLI on macOS and Linux
command -v gemini-api >/dev/null 2>&1 || (curl -fsSL https://raw.githubusercontent.com/google-gemini/gemini-api-cli/main/scripts/install.sh | bash)
# Windows (PowerShell): iwr -useb https://raw.githubusercontent.com/google-gemini/gemini-api-cli/main/scripts/install.ps1 | iex

# 2a. Authenticate with a Gemini API Key (environment variable or OS keychain)
export GEMINI_API_KEY="your-api-key"
# Or store credentials in the OS keychain:
gemini-api configure --api-key "your-api-key"

# 2b. Or authenticate with a Google Cloud Project / ADC (OAuth2 bearer token)
# Note: gemini-api does NOT auto-discover application_default_credentials.json, and
# generativelanguage.googleapis.com requires the 'generative-language' OAuth scope
# (standard 'gcloud auth application-default login' fails with HTTP 403 ACCESS_TOKEN_SCOPE_INSUFFICIENT).
gcloud auth application-default login \
  --scopes="https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language"
export GEMINI_ACCESS_TOKEN="$(gcloud auth application-default print-access-token)"
export GEMINI_USER_PROJECT="your-gcp-project-id"
# (Or pass per-command: --access-token "$(gcloud auth application-default print-access-token)" --user-project "your-gcp-project-id")

# 3. Verify credentials and inspect CLI video capabilities
gemini-api whoami
gemini-api models
gemini-api video --help
gemini-api video --schema

# Generate a 1080p 16:9 video directly to disk (prints only the saved file path to stdout)
gemini-api video \
  "A cinematic wide shot of a snow leopard stalking across a rocky Himalayan ridge at dusk, howling wind and crunching snow" \
  --resolution "1080p" \
  --aspect-ratio "16:9" \
  --out "./assets/snow_leopard.mp4"

# Animate a starting frame generated with gemini-api image (Nano Banana -> Video pipeline)
START_IMG=$(gemini-api image "An origami paper boat resting on a mossy forest stream, macro photography" --aspect-ratio "16:9" --out "./assets/boat_frame.png")
gemini-api video \
  "<FIRST_FRAME> The paper boat drifts gently downstream through dappled sunlight, babbling brook water sounds. Use this image as the starting frame." \
  -i "$START_IMG" \
  --resolution "1080p" \
  --out "./assets/origami_boat.mp4"

BASE_URL="https://generativelanguage.googleapis.com/v1beta"

OP_NAME=$(curl -sf "${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 shot of a vintage brass espresso machine pulling a rich crema shot in a sunlit Italian cafe, hissing steam and clinking porcelain cups."
    }],
    "parameters": {
      "aspectRatio": "16:9",
      "resolution": "1080p",
      "durationSeconds": 8,
      "negativePrompt": "cartoon, drawing, low quality, blurry"
    }
  }' | jq -r '.name')

while true; do
  STATUS_JSON=$(curl -sf -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${OP_NAME}")
  if [ "$(echo "$STATUS_JSON" | jq -r '.done')" = "true" ]; then
    VIDEO_URI=$(echo "$STATUS_JSON" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    curl -sfL -H "x-goog-api-key: $GEMINI_API_KEY" -o "./assets/espresso.mp4" "$VIDEO_URI"
    echo "./assets/espresso.mp4"
    break
  fi
  sleep 10
done

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

client = genai.Client()

prompt = (
    "A medium two-shot inside an antique clockmaker's workshop. "
    "An elderly clockmaker holds a brass magnifying loupe and whispers, "
    "\"Every gear remembers the century it was forged.\" "
    "Soft ticking of dozens of wall clocks and a warm chime in the background."
)

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
    config=types.GenerateVideosConfig(
        aspect_ratio="16:9",
        resolution="1080p",
        duration_seconds=8,
        negative_prompt="modern electronics, neon lights, blurry, watermark",
    ),
)

while not operation.done:
    time.sleep(10)
    operation = client.operations.get(operation)

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

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

client = genai.Client()

first_image = types.Image.from_file(location="first_frame.png")
last_image = types.Image.from_file(location="last_frame.png")

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="A smooth camera dolly-in as the wooden treasure chest slowly creaks open, revealing a warm golden glow inside.",
    image=first_image,
    config=types.GenerateVideosConfig(
        last_frame=last_image,
        duration_seconds=8,
        aspect_ratio="16:9",
    ),
)

while not operation.done:
    time.sleep(10)
    operation = client.operations.get(operation)

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

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

client = genai.Client()

character_ref = types.VideoGenerationReferenceImage(
    image=types.Image.from_file(location="hero_character.png"),
    reference_type="asset",
)
vehicle_ref = types.VideoGenerationReferenceImage(
    image=types.Image.from_file(location="hoverbike.png"),
    reference_type="asset",
)

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="Medium tracking shot of the hero riding the hoverbike across a salt flat at golden hour, turbine engine hum and rushing wind.",
    config=types.GenerateVideosConfig(
        reference_images=[character_ref, vehicle_ref],
        duration_seconds=8,
        aspect_ratio="16:9",
    ),
)

while not operation.done:
    time.sleep(10)
    operation = client.operations.get(operation)

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

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

client = genai.Client()

# Pass `video.video` from a completed Veo generation operation
extension_op = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    video=video.video,
    prompt="The hoverbike banks sharply around a sandstone spire and accelerates into a canyon.",
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        resolution="720p",
    ),
)

while not extension_op.done:
    time.sleep(10)
    extension_op = client.operations.get(extension_op)

extended_video = extension_op.response.generated_videos[0]
client.files.download(file=extended_video.video)
extended_video.video.save("hoverbike_extended.mp4")