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Luma Ray 3.2 Text to Video API

luma /

Luma Ray 3.2 Text to Video is a fast AI video generation model that creates cinematic videos from text prompts with controllable aspect ratio, resolution, duration, and optional reference images. Ready-to-use REST inference API for cinematic clips, storytelling videos, social media content, advertising creatives, product visuals, concept videos, and professional text-to-video workflows with simple integration, no coldstarts, and affordable pricing.

text-to-video
इनपुट

निष्क्रिय

$0.5प्रति रन·~20 / $10

आगे:

उदाहरणसभी देखें

A cinematic post-apocalyptic video of a tired father carrying his little daughter through an abandoned city street at sunrise. Broken cars, overgrown plants, and ruined buildings surround them. The daughter wakes up and points at a small butterfly landing on a cracked traffic light. The father stops, smiles faintly for the first time, and gently lowers her to watch it. The camera follows them from behind, then slowly circles to reveal their faces. Warm sunrise light, dust in the air, realistic survival drama, emotional and hopeful tone. No subtitles, no text, no watermark.

संबंधित मॉडल

README

Luma Ray 3.2 Text-to-Video

Luma Ray 3.2 Text-to-Video generates cinematic videos from natural-language prompts with selectable aspect ratio, resolution, duration, and optional reference guidance. It is suitable for story-driven scenes, ad concepts, cinematic previsualization, social content, and other prompt-based video generation workflows.

Why Choose This?

  • Prompt-based video generation
    Turn detailed natural-language scene descriptions into generated video clips.

  • Multiple resolution tiers
    Choose 540p, 720p, or 1080p depending on your quality and budget needs.

  • Simple duration control
    Generate either 5s or 10s clips with predictable pricing.

  • Flexible aspect ratio
    Use different size presets such as 16:9 for widescreen output.

  • Optional reference support
    Add references when you want stronger visual guidance for style, subject, or scene direction.

  • Production-ready workflow
    Useful for trailers, short-form storytelling, creative prototyping, and commercial concept videos.

Parameters

ParameterRequiredDescription
promptYesText prompt describing the scene, motion, camera behavior, and visual style.
sizeNoOutput aspect ratio preset, such as 16:9.
resolutionNoOutput resolution. Supported values: 540p, 720p, 1080p.
durationNoOutput duration. Supported values: 5s, 10s.
referenceNoOptional reference input for additional visual guidance.

How to Use

  1. Write your prompt — describe the subject, setting, action, camera movement, lighting, and mood you want.
  2. Choose size — select the aspect ratio that fits your target platform or layout.
  3. Choose resolution — use 540p for the lowest cost, 720p for a middle option, or 1080p for the highest quality.
  4. Choose duration — select either 5s or 10s.
  5. Add references (optional) — include references if you want extra control over visual direction.
  6. Submit — run the model and download the generated video.

Example Prompt

A cinematic post-apocalyptic video of a tired father carrying his little daughter through an abandoned city street at sunrise. Broken cars, overgrown plants, and ruined buildings surround them. The daughter wakes up and points at a small butterfly landing on a cracked traffic light. The father stops, smiles faintly for the first time, and gently lowers her to watch it. The camera follows them from behind, then slowly circles to reveal their faces. Warm sunrise light, dust in the air, realistic survival drama.

Pricing

Pricing depends on duration and resolution.

Duration540p720p1080p
5s$0.50$1.00$2.00
10s$1.00$2.00$4.00

Billing Rules

  • Base price is $0.50 for a 5s video at 540p
  • 10s costs the 5s rate
  • 720p costs the 540p rate
  • 1080p costs the 540p rate
  • Pricing depends only on duration and resolution

Best Use Cases

  • Cinematic scene generation — Create short dramatic or emotional video moments from prompts.
  • Trailer concepts — Prototype story beats and visual sequences quickly.
  • Social media content — Generate short-form video ideas for vertical or widescreen publishing.
  • Advertising concepts — Build mood-driven commercial shots before full production.
  • Creative previsualization — Test scene direction, pacing, and camera ideas.

Pro Tips

  • Be specific in your prompt about subject motion, camera movement, lighting, and emotional tone.
  • Use 5s for fast concept validation, then switch to 10s when you need more scene development.
  • Start with 540p or 720p for testing and move to 1080p for higher-end final outputs.
  • Add references only when you need stronger visual steering.
  • Keep prompts focused on what should happen in the clip rather than listing unrelated visual details.

Notes

  • prompt is required.
  • duration currently supports 5s and 10s.
  • Pricing depends only on the selected duration and resolution.
  • Reference input is optional.

Related Models

  • Luma image-to-video workflows — Useful when you want to animate a starting image instead of generating directly from text.
  • Luma video editing workflows — Useful when you want to transform an existing video rather than create one from scratch.
  • Prompt-based video generation workflows — Useful when you want alternative quality, cost, or motion styles.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

Ray 3.2 Text To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/luma/ray-3.2/text-to-video 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 3.2 Text To Video below.

HTTP example
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",
    "size": "16:9",
    "resolution": "540p",
    "duration": "5s"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/luma/ray-3.2/text-to-video" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/luma/ray-3.2/text-to-video";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "size": "16:9",
        "resolution": "540p",
        "duration": "5s"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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",
    "size": "16:9",
    "resolution": "540p",
    "duration": "5s"
}

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-3.2/text-to-video", 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 3.2 Text To Video API — Frequently asked questions

What is the Ray 3.2 Text To Video API?

Ray 3.2 Text To Video is a Luma model for video generation, exposed as a REST API on WaveSpeedAI. Luma Ray 3.2 Text to Video is a fast AI video generation model that creates cinematic videos from text prompts with controllable aspect ratio, resolution, duration, and optional reference images. Ready-to-use REST inference API for cinematic clips, storytelling videos, social media content, advertising creatives, product visuals, concept videos, and professional text-to-video workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Ray 3.2 Text To Video API?

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-3.2-text-to-video.

How much does Ray 3.2 Text To Video cost per run?

Ray 3.2 Text To Video starts at $0.50 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.

What inputs does Ray 3.2 Text To Video accept?

Key inputs: `prompt`, `resolution`, `duration`, `size`, `reference`. 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-3.2-text-to-video.

How long does Ray 3.2 Text To Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 41 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Ray 3.2 Text To Video outputs commercially?

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.

Luma Ray 3.2 Text to Video API | WaveSpeedAI