Vidu Q2 Pro Start-End to Video produces smooth image-to-video transitions between start and end images for seamless morphs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.15per run·~66 / $10
The buds rapidly bloom, petals opening and swirling around her as the camera circles.
A black and white line drawing of a couple taking a selfie, begins to fill with soft, watercolor-like colors. The background remains simple. The lines start to soften, and a warm light begins to glow from within the characters.
Zoom in slowly.
The character turns around and suddenly transforms into a figurine.
As the car speeds forward, its paint shimmers and gradually changes color with motion blur.
The forest lights up as the wizard casts spells, colorful magic energy swirling around, fireflies and mystical lights appearing, slow camera pan, dynamic motion --v 5
Wind blows through the room, lifting papers, which transform into fluttering colorful butterflies.
Each raindrop hits the ground and turns into tiny sparkling light particles.
Stars begin to twinkle and streak as shooting stars appear, sky slowly becoming alive.
Generate a coherent shot from just two images: a start frame and an end frame. Vidu Q2 Pro infers natural, object-aware motion between them, making it perfect for scene transitions, bridging shots, and visual storytelling. No local setup required.
| Resolution | Duration | Pricing |
|---|---|---|
| 540p | 1s | $0.04 |
| 540p | 2s | $0.05 |
| 540p | 3s | $0.075 |
| 540p | 4s | $0.10 |
| 540p | 5s | $0.125 |
| 540p | 6s | $0.15 |
| 540p | 7s | $0.175 |
| 540p | 8s | $0.20 |
| 720p | 1s | $0.075 |
| 720p | 2s | $0.125 |
| 720p | 3s | $0.175 |
| 720p | 4s | $0.225 |
| 720p | 5s | $0.275 |
| 720p | 6s | $0.325 |
| 720p | 7s | $0.375 |
| 720p | 8s | $0.425 |
| 1080p | 1s | $0.275 |
| 1080p | 2s | $0.35 |
| 1080p | 3s | $0.425 |
| 1080p | 4s | $0.50 |
| 1080p | 5s | $0.575 |
| 1080p | 6s | $0.65 |
| 1080p | 7s | $0.725 |
| 1080p | 8s | $0.80 |
Our accelerated inference approach leverages advanced optimization technology from WaveSpeedAI. This fusion technique reduces computational overhead and latency, enabling rapid generation without compromising quality. The system is tuned for large-scale workloads while keeping real-time use cases snappy and reliable. For implementation details, see our engineering blog post.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-q2-pro with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Start End To Video Q2 Pro below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"resolution": "720p",
"bgm": true,
"movement_amplitude": "auto",
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-q2-pro" \
-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/start-end-to-video-q2-pro";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"resolution": "720p",
"bgm": true,
"movement_amplitude": "auto",
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"resolution": "720p",
"bgm": True,
"movement_amplitude": "auto",
"seed": -1
}
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/start-end-to-video-q2-pro", 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)Start End To Video Q2 Pro is a Vidu model for video generation from images, exposed as a REST API on WaveSpeedAI. Vidu Q2 Pro Start-End to Video produces smooth image-to-video transitions between start and end images for seamless morphs. 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-start-end-to-video-q2-pro.
Start End To Video Q2 Pro 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`, `image`, `resolution`, `duration`, `seed`, `bgm`. 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-start-end-to-video-q2-pro.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
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.