Luma API
Luma API on WaveSpeedAI — Ray 3.2 text-to-video, image-to-video, video edit and video reframing, earlier Ray 2 and Ray 1.6 models, and Uni v1 and Photon image generation and editing, all through one REST API.
Use Luma's Ray 3.2 for cinematic text-to-video and image-to-video with controllable aspect ratio, resolution and duration, re-render or reframe existing footage with video edit and video reframing, and generate or edit images with Uni v1 and Photon. Ray 2 and Ray 1.6 stay available for earlier workflows.
Overview
About the Luma API
What Luma does, how it fits in the Luma AI model lineup, and why teams reach for it.
Luma is a video generation model from Luma AI, available through the WaveSpeedAI REST API. Luma API on WaveSpeedAI — Ray 3.2 text-to-video, image-to-video, video edit and video reframing, earlier Ray 2 and Ray 1.6 models, and Uni v1 and Photon image generation and editing, all through one REST API.
Use Luma's Ray 3.2 for cinematic text-to-video and image-to-video with controllable aspect ratio, resolution and duration, re-render or reframe existing footage with video edit and video reframing, and generate or edit images with Uni v1 and Photon. Ray 2 and Ray 1.6 stay available for earlier workflows.
The Luma family on WaveSpeedAI ships 6 REST endpoints covering Video-To-Video, Image-To-Video, Text-To-Video, Image-To-Image, Text-To-Image workflows. Each variant carries its own pricing, parameter knobs, and example outputs — pick the one that matches your input modality and production constraints, or call several from the same API key to compose multi-step pipelines.
Run Luma through the same API key, billing account, and rate-limit envelope you use for the other 1,000+ AI models on WaveSpeedAI. No separate vendor setup, no per-provider SDKs, no per-vendor rate-limit envelopes — one integration covers everything from text-to-image and text-to-video through audio synthesis, 3D generation, upscaling, and editing.
Endpoints
All Luma API endpoints
6 Luma endpoints available now on WaveSpeedAI — pick the variant that matches your workflow.
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Ray 3.2 Video Edit
Luma Ray 3.2 Video Edit is a fast AI video-to-video editing model that re-renders an existing source video from a text prompt while preserving the original motion and timing. Ready-to-use REST inference API for video restyling, creative edits, product videos, advertising creatives, social media clips, visual storytelling, and professional video editing workflows with simple integration, no coldstarts, and affordable pricing.
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Ray 3.2 Video Reframing
Luma Ray 3.2 Video Reframing is a fast AI video reframing model that changes an existing video’s aspect ratio and fills the newly exposed canvas from a prompt while preserving the source footage. Ready-to-use REST inference API for aspect ratio conversion, social media resizing, cinematic reframing, video outpainting, creator content, advertising creatives, and professional video editing workflows with simple integration, no coldstarts, and affordable pricing.
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Ray 3.2 Image To Video
Luma Ray 3.2 Image to Video is a fast AI image-to-video generation model that animates a source image into cinematic video guided by a text prompt, with controllable aspect ratio, resolution, and duration. Ready-to-use REST inference API for image animation, cinematic clips, product videos, social media content, advertising creatives, visual storytelling, and professional image-to-video workflows with simple integration, no coldstarts, and affordable pricing.
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Ray 3.2 Text To Video
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, and duration. 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.
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Uni v1 Edit
Luma Uni v1 Edit is a fast AI image editing model that reworks source images from text instructions while preserving composition and supporting optional reference images. Ready-to-use REST inference API for photo editing, product image updates, creative retouching, marketing assets, brand visuals, style changes, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing.
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Uni v1 Text To Image
Luma Uni v1 Text to Image is a fast AI image generation model that creates high-fidelity images from prompts with flexible aspect ratios, visual style control, and optional reference-image guidance. Ready-to-use REST inference API for creative design, marketing visuals, product mockups, brand assets, social media content, concept art, and professional text-to-image workflows with simple integration, no coldstarts, and affordable pricing.
Examples
See Luma in action
Real outputs generated by the Luma API. Hover any video to preview, click to open the full-size viewer.
How to
How to use the Luma API
Four steps from signup to a finished generation. Full Python, Node.js, and cURL examples are in the API section below.
- 01
Get an API key
Sign up for a WaveSpeedAI account and copy your API key from the dashboard. New accounts come with free starter credits — enough to run the playground a few dozen times before billing kicks in.
- 02
Submit a prediction
POST your input as JSON to https://api.wavespeed.ai/api/v3/luma/ray-3.2/text-to-video. The endpoint returns a prediction id immediately — generations are async so you don't hold an open connection during inference.
- 03
Poll for completion
GET https://api.wavespeed.ai/api/v3/predictions/{request_id}/result. Return outputs on completed; stop with an error on failed, cancelled, timeout, or deleted; continue polling for every other status.
- 04
Read the output URL
Once status is "completed", read the URL from data.outputs[0]. The URL points to your generated media on the WaveSpeedAI CDN — image, video, audio, or 3D file depending on the Luma variant you called.
Use cases
What you can build with Luma
Common workflows developers and creators use the Luma API for.
Cinematic text-to-video
luma/ray-3.2/text-to-video generates cinematic videos from text prompts with controllable aspect ratio, resolution, duration and optional reference images.
Animate a still image
luma/ray-3.2/image-to-video animates a source image into video guided by a text prompt, with the same aspect ratio, resolution and duration controls.
Restyle footage and keep the motion
luma/ray-3.2/video-edit re-renders an existing source video from a text prompt while preserving the source motion and timing.
Reframe video to a new aspect ratio
luma/ray-3.2/video-reframing changes a video's aspect ratio and fills the newly exposed canvas from a prompt while preserving the source footage.
Image generation and editing
luma/uni-v1/text-to-image generates images with aspect ratio, style, format and reference-image guidance; luma/uni-v1/edit transforms an input image from a text instruction while preserving composition.
Tips
Tips for prompting Luma
Practical advice for getting better outputs from Luma — drawn from the patterns that work across video models in production pipelines.
- 01
Be specific about camera moves
Mention concrete cinematography vocabulary — orbit, dolly-in, push-in, pan-left, crane shot, handheld follow. Generic prompts produce static or arbitrary camera choices; named camera moves map directly to motion intent in the model's training data and dramatically improve shot quality.
- 02
Anchor character identity with reference images
If your prompt depends on a specific person, character, or product, upload a reference image alongside the prompt. Without a reference, identity drifts across frames and across shots — the same character ends up looking like a slightly different person each generation.
- 03
Describe lighting and time of day
Lighting cues like 'golden hour, soft warm directional light' or 'overcast diffused light, slate-grey sky' improve quality and consistency far more than vague quality modifiers. Lighting is one of the strongest priors the model conditions on.
- 04
Use negative prompts to suppress common failure modes
Useful negatives for video: 'frame flicker, motion blur, watermark, text artifacts, distorted hands, low resolution, jpeg compression'. Negative prompts cost nothing and noticeably reduce the rate of generations you'd otherwise re-roll.
- 05
Pick the shortest duration that captures your beat
Most prompts work best at 5-8 seconds. Longer clips amplify temporal inconsistencies (subject morphing, environment drift). If you need a 20-second sequence, generate three 6-8 second clips and edit them together — quality stays higher than one long generation.
- 06
Match aspect ratio to platform up front
9:16 for TikTok / Reels / Shorts, 16:9 for landscape feeds and YouTube, 1:1 for post grids. Models train slightly differently per aspect ratio — cropping a 16:9 to 9:16 after the fact loses both fidelity and the composition the model intended.
Pricing
Luma API pricing
Pricing is per-output. The final charge scales with the parameters you set in each variant's playground (resolution, duration, output count, references).
| Endpoint | Type | Starting price |
|---|---|---|
| luma/ | video-to-video | $0.144 |
| luma/ | video-to-video | $0.06 |
| luma/ | image-to-video | $0.10 |
| luma/ | text-to-video | $0.10 |
| luma/ | image-to-image | $0.042 |
| luma/ | text-to-image | $0.042 |
API
Call the Luma API
Sign up for an API key at wavespeed.ai/accesskey, then submit a prediction via REST. The playground generates ready-to-paste samples for any combination of inputs.
POSThttps://api.wavespeed.ai/api/v3/luma/ray-3.2/text-to-video
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(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 $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "16:9",
"resolution": "540p",
"duration": 5
}')
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst 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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "16:9",
"resolution": "540p",
"duration": 5
}),
});
const task = body.data ?? body;
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
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",
"size": "16:9",
"resolution": "540p",
"duration": 5
}
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)
result_url = 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", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Compare
Luma vs alternatives
When to pick Luma over similar models on WaveSpeedAI.
Luma vs Kling 3.0
Kling 3.0 offers Standard, Pro and 4K tiers with native audio. Luma Ray 3.2 adds video edit and video reframing endpoints for working on existing footage.
Luma vs Veo 3.1
Veo 3.1 is Google's video model with synchronized audio. Luma Ray 3.2 is a separate family with optional reference images and footage-level edit and reframing endpoints.
FAQ
Luma API — Frequently asked questions
Pricing, license, integration — common questions about running Luma on WaveSpeedAI.
What is the Luma API?
Luma is a Luma AI video generation model exposed as a REST API on WaveSpeedAI. Luma API on WaveSpeedAI — Ray 3.2 text-to-video, image-to-video, video edit and video reframing, earlier Ray 2 and Ray 1.6 models, and Uni v1 and Photon image generation and editing, all through one REST API. You can call it programmatically or try it from the playground linked above.
How do I call the Luma API?
Sign up for a WaveSpeedAI account, copy your API key from /accesskey, then POST to https://api.wavespeed.ai/api/v3/luma/ray-3.2/text-to-video with your input as JSON. The endpoint 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. Production-oriented Python / Node.js / cURL examples are above.
How much does the Luma API cost?
Luma starts at $0.042. The exact cost scales with the parameters you set (resolution, duration, output count, references). The live cost preview next to the Generate button in the playground shows the exact price for your current input.
Which Luma variants are available?
WaveSpeedAI hosts 6 live Luma endpoints: luma/ray-3.2/video-edit, luma/ray-3.2/video-reframing, luma/ray-3.2/image-to-video, luma/ray-3.2/text-to-video, luma/uni-v1/edit, luma/uni-v1/text-to-image. Each variant has its own playground page and pricing.
Can I use Luma outputs commercially?
Commercial usage rights follow the Luma AI model license. Most Luma AI models permit commercial output use; see each model's playground page for the specific license summary, and WaveSpeedAI's Terms of Service for platform-level conditions.
Why use Luma on WaveSpeedAI instead of going direct?
One API key + one billing account across Luma AND 1,000+ other AI models from other providers. No per-vendor SDK setup, no separate rate-limit envelopes, no rewrite-per-vendor integration code. Pricing is typically at parity with or below Luma AI's direct API.
Start building with Luma on WaveSpeedAI
Free starter credits on signup. One API key across 1,000+ AI models from Luma AI and every other provider.
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