from runwayml import RunwayML

client = RunwayML()

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import RunwayML from "@runwayml/sdk";

const client = new RunwayML();

// Image-to-video with latest Gen-4.5 model
const task = await client.imageToVideo
  .create({
    model: "gen4.5",
    promptImage: "https://example.com/image.jpg",
    promptText: "A timelapse on a sunny day with clouds flying by",
    ratio: "1280:720",
    duration: 10,
  })
  .waitForTaskOutput();

console.log(`Video URL: ${task.output[0]}`);

// Text-to-video (no image required)
const textTask = await client.imageToVideo
  .create({
    model: "gen4.5",
    promptText: "A serene mountain landscape at sunset",
    ratio: "1280:720",
    duration: 10,
  })
  .waitForTaskOutput();

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---

task = client.image_to_video.create(
    model="gen4.5",
    prompt_text="A serene mountain lake at sunrise with mist rising from the water",
    ratio="1280:720",
    duration=10,
    seed=12345  # Optional: reproducibility
).wait_for_task_output()

task = client.image_to_video.create(
    model="gen4.5",
    prompt_image="https://example.com/image.jpg",
    prompt_text="Camera slowly pushes in, leaves rustling in the breeze",
    ratio="1584:672",
    duration=10,
    seed=12345
).wait_for_task_output()

task = client.image_to_video.create(
    model="gen4_turbo",
    prompt_image="https://example.com/image.jpg",
    prompt_text="Camera slowly pushes in, leaves rustling",
    ratio="1280:720",
    duration=5,
    seed=12345           # Optional: reproducibility
).wait_for_task_output()

task = client.video_to_video.create(
    model="gen4_aleph",
    video_uri="https://example.com/source.mp4",
    prompt_text="Transform to anime style",
    references=[{"uri": "https://example.com/style_ref.jpg"}]  # Optional style reference
).wait_for_task_output()

task = client.character_performance.create(
    model="act_two",
    character={"type": "image", "uri": "https://example.com/character.jpg"},  # or type: "video"
    reference={"type": "video", "uri": "https://example.com/performance.mp4"}
).wait_for_task_output()

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> **Note:** Gen-4.5 and Gen-4 models produce silent video. Use Veo if you need audio, or add audio separately with ElevenLabs models (see Audio Generation section).

---

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**Note:** `gen4_image_turbo` requires `reference_images` (at least one). Use `gen4_image` for text-only generation.

Untagged references apply as general style:

```python
reference_images=[
    {"uri": "https://example.com/subject.jpg", "tag": "subject"},
    {"uri": "https://example.com/style.jpg"}  # No tag = style reference
]

task = client.text_to_speech.create(
    model="eleven_multilingual_v2",
    prompt_text="Hello, welcome to RunwayML!",
    voice={"type": "runway-preset", "preset_id": "Maya"}
).wait_for_task_output()

task = client.sound_effect.create(
    model="eleven_text_to_sound_v2",
    prompt_text="Thunder rumbling in the distance, rain on a window"
).wait_for_task_output()

task = client.voice_isolation.create(
    model="eleven_voice_isolation",
    audio_uri="https://example.com/noisy_audio.mp3"
).wait_for_task_output()

task = client.voice_dubbing.create(
    model="eleven_voice_dubbing",
    audio_uri="https://example.com/speech.mp3",
    target_lang="es"  # Spanish
).wait_for_task_output()

task = client.speech_to_speech.create(
    model="eleven_multilingual_sts_v2",
    media={"type": "audio", "uri": "https://example.com/original.mp3"},  # or type: "video"
    voice={"type": "runway-preset", "preset_id": "Maya"}
).wait_for_task_output()

import base64

with open("image.jpg", "rb") as f:
    data_uri = f"data:image/jpeg;base64,{base64.b64encode(f.read()).decode()}"

task = client.image_to_video.create(
    model="gen4_turbo",
    prompt_image=data_uri,
    prompt_text="Gentle movement"
).wait_for_task_output()

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See [Task Management](references/task-management.md) for manual polling and batch processing.

---

from runwayml import RunwayML, APIError, RateLimitError

client = RunwayML()

try:
    task = client.image_to_video.create(...).wait_for_task_output()
    if task.status == "FAILED":
        print(f"Generation failed: {task.failure}")
except RateLimitError:
    print("Rate limited - SDK retries automatically")
except APIError as e:
    print(f"API error {e.status_code}: {e.message}")