Vidu Q3 Ad Video generates commercial ad videos from 1 to 7 reference images with prompt guidance, supporting 720P / 1080P output and synchronized audio for product ads, brand campaigns, marketing creatives, and promotional videos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.15실행당·~66 / $10
The perfume bottle remains centered while soft mist rolls across the black marble surface. Rose petals drift slowly through the air, a beam of light sweeps across the glass, and the liquid inside catches elegant reflections. Smooth luxury commercial camera movement, refined cinematic lighting, premium beauty advertisement style.
Vidu Q3 Ad generates ad-oriented videos from one or more reference images and a text prompt. It is designed for product, character, and commercial scene generation, where uploaded references guide the subject appearance, visual style, and overall consistency of the generated video.
Ad-focused reference video generation
Use reference images to guide product, character, or scene appearance for commercial-style videos.
Multi-image reference support
Upload 1 to 7 images to provide visual references for the generated video.
Flexible video length
Generate clips from 3 to 16 seconds.
720p and 1080p output
Choose 720p for lower-cost iteration or 1080p for higher-quality ad output.
Optional synchronized audio
Generate audio together with the video when the prompt calls for ambience, speech, or sound effects.
| Parameter | Required | Description |
|---|---|---|
| images | Yes | Reference images for video generation. Use 1 to 7 images. |
| prompt | Yes | Text prompt describing the ad scene, subject action, camera movement, and style. |
| aspect_ratio | No | Output aspect ratio. Default: 16:9. |
| resolution | No | Output resolution: 720p or 1080p. Default: 720p. |
| duration | No | Video duration in seconds. Range: 3 to 16. Default: 5. |
| generate_audio | No | Generate synchronized audio with the video. Default: true. |
| seed | No | Random seed. Use 0 for a random seed. |
720p for lower-cost iteration or 1080p for higher-quality output.3 and 16 seconds.generate_audio enabled when synchronized audio is needed.Pricing is based on selected resolution and duration.
| Resolution | Price per second | 5s | 10s | 16s |
|---|---|---|---|---|
| 720p | $0.15 | $0.75 | $1.50 | $2.40 |
| 1080p | $0.18 | $0.90 | $1.80 | $2.88 |
720p for prompt iteration before switching to 1080p.seed when you need to reproduce or compare variations.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/vidu/q3-ad with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Q3 Ad below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"generate_audio": true,
"seed": 0
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/vidu/q3-ad" \
-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/vidu/q3-ad";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"generate_audio": true,
"seed": 0
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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 = {
"images": [
"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",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"generate_audio": True,
"seed": 0
}
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/vidu/q3-ad", 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)Q3 Ad is a Vidu model for video generation from images, exposed as a REST API on WaveSpeedAI. Vidu Q3 Ad Video generates commercial ad videos from 1 to 7 reference images with prompt guidance, supporting 720P / 1080P output and synchronized audio for product ads, brand campaigns, marketing creatives, and promotional videos. 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/vidu/vidu-q3-ad.
Q3 Ad starts at $0.15 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`, `images`, `aspect_ratio`, `resolution`, `duration`, `seed`. 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/vidu/vidu-q3-ad.
Median end-to-end generation time on WaveSpeedAI is around 635 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 (Vidu). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.