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, duration, and optional reference images. 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.
निष्क्रिय
$0.5प्रति रन·~20 / $10
A cinematic video of a teenage girl crossing a vast desert at sunset, dragging a small cart filled with scrap metal. She discovers a damaged robot half-buried in the sand. At first she points a tool at it cautiously, but the robot weakly raises one hand and offers her a tiny glowing flower made of metal. She slowly lowers her guard and smiles. The camera starts with a wide desert landscape, then moves into a gentle close-up of the girl and the robot. Golden sunset, blowing sand, emotional sci-fi adventure, warm and magical tone. No subtitles, no text, no watermark.
Luma Ray 3.2 Image-to-Video turns a reference image into a cinematic video clip using a natural-language prompt. It is suitable for animating concept art, character shots, illustrated scenes, marketing visuals, and story-driven still images with controllable duration and resolution.
Image-guided video generation
Start from a single image and animate it into a short cinematic clip.
Prompt-based motion control
Describe subject motion, camera movement, scene evolution, and atmosphere in natural language.
Multiple resolution tiers
Choose 540p, 720p, or 1080p depending on your quality and budget needs.
Simple duration options
Generate either 5s or 10s clips with predictable pricing.
Flexible aspect ratio
Use size presets such as 16:9 for widescreen output.
Optional reference support
Add references when you want additional visual guidance for style, subject, or scene consistency.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Input image to animate. |
| last_image | No | Optional ending image used to guide the final frame or target end state of the generated video. |
| prompt | Yes | Text prompt describing the motion, camera behavior, and scene development. |
| size | No | Output aspect ratio preset, such as 16:9. |
| resolution | No | Output resolution. Supported values: 540p, 720p, 1080p. |
| duration | No | Output duration. Supported values: 5s, 10s. |
| reference | No | Optional reference input for additional visual guidance. |
540p for lower cost, 720p for balanced output, or 1080p for higher quality.5s or 10s.A cinematic video of a teenage girl crossing a vast desert at sunset, dragging a small cart filled with scrap metal. She discovers a damaged robot half-buried in the sand. At first she points a tool at it cautiously, but the robot weakly raises one hand and offers her a tiny glowing flower made of metal. She slowly lowers her guard and smiles. The camera starts with a wide desert landscape, then moves into a gentle close-up of the girl and the robot. Golden sunset, blowing sand, emotional sci-fi adventure, warm and hopeful tone.
Pricing depends on duration and resolution.
| Duration | 540p | 720p | 1080p |
|---|---|---|---|
| 5s | $0.50 | $1.00 | $2.00 |
| 10s | $1.00 | $2.00 | $4.00 |
5s video at 540p10s costs 2× the 5s rate720p costs 2× the 540p rate1080p costs 4× the 540p rateduration and resolution5s for faster iteration, then move to 10s when you want more scene development.540p or 720p for testing and 1080p for higher-end output.image and prompt are required.duration currently supports 5s and 10s.duration and resolution.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/luma/ray-3.2/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Ray 3.2 Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "16:9",
"resolution": "540p",
"duration": "5s"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/luma/ray-3.2/image-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/luma/ray-3.2/image-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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "16:9",
"resolution": "540p",
"duration": "5s"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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 = {
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"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/image-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 Image To Video is a Luma model for video generation from images, exposed as a REST API on WaveSpeedAI. 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, duration, and optional reference images. 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. 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-3.2-image-to-video.
Ray 3.2 Image 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.
Key inputs: `prompt`, `image`, `resolution`, `duration`, `size`, `last_image`. 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-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 48 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.