Luma Ray 1.6 turns images into high-quality videos in multiple sizes with advanced prompt optimization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Boşta
$0.2çalıştırma başına·~50 / $10
A man in a hooded jacket climbs a rugged mountain path at dawn, breath misting in the cold air. The rising sun casts long shadows over snow-dusted peaks. Wide-angle shot reveals vast alpine scenery, followed by close-up of his determined face. Wind rustles his backpack straps. Sound of crunching snow underfoot and distant eagle cries. Epic orchestral undertone, dynamic movement through drone tracking shots.
Evening stroll through a quiet neighborhood, porch lights on, cats crossing the street, sound of cicadas and distant TV through windows
Young couple watching an old movie on the couch, popcorn bowl in hand, dim lighting, blanket over their laps, soft flickering screen glow
Barista closing up shop, cleaning the espresso machine, wiping down counters, turning off lights one by one, peaceful city night outside
Parents baking cookies with kids in a cozy kitchen, flour flying, chocolate chips scattered, warm oven light and holiday mood
Teenage siblings watching TV in their shared bedroom, legs tangled in blankets, popcorn everywhere, screen reflections in their eyes
Middle-aged woman drinking coffee alone on a balcony at sunrise, city skyline slowly waking up, soft breeze and chirping birds
Old man sitting at a café terrace watching people pass by, sipping espresso, glasses on newspaper, golden afternoon sun
Teen girl window shopping in a small downtown street, colorful storefronts, reflected lights, city bustle around her
Barista preparing a latte in a busy café, milk steaming, customers chatting, close-up of latte art being poured
Transform images into cinematic videos with Luma Ray 1.6 — the latest generation of Luma's acclaimed video generation model. Known for exceptional camera work, dramatic lighting, and cinematic quality, Ray 1.6 brings still images to life with professional-grade motion and atmosphere.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source image to animate (upload or public URL). |
| prompt | Yes | Text description of the motion, camera work, and atmosphere. |
| size | No | Output dimensions: 1280×720 (landscape) or 720×1280 (portrait). Default: 1280×720. |
| duration | No | Video length: 5 or 10 seconds. Default: 5. |
| Enable Safety Checker | No | Toggle content safety filtering. |
Per 5-second billing based on duration.
| Duration | Calculation | Cost |
|---|---|---|
| 5 seconds | 5 ÷ 5 × $0.20 | $0.20 |
| 10 seconds | 10 ÷ 5 × $0.20 | $0.40 |
| Size | Orientation | Best For |
|---|---|---|
| 1280×720 | Landscape | YouTube, presentations, cinematic content |
| 720×1280 | Portrait | TikTok, Instagram Reels, Stories, mobile |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/luma/ray-1.6-i2v with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Ray 1.6 I2v below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1280*720",
"duration": 10
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/luma/ray-1.6-i2v" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/luma/ray-1.6-i2v";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1280*720",
"duration": 10
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1280*720",
"duration": 10
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/luma/ray-1.6-i2v", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Ray 1.6 I2v is a Luma model for video generation from images, exposed as a REST API on WaveSpeedAI. Luma Ray 1.6 turns images into high-quality videos in multiple sizes with advanced prompt optimization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/luma/luma-ray-1.6-i2v.
Ray 1.6 I2v starts at $0.20 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `image`, `duration`, `size`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/luma/luma-ray-1.6-i2v.
Median end-to-end generation time on WaveSpeedAI is around 113 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Luma). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.