Vidu Q2 Turbo Start-End to Video creates smooth Image-to-Video transitions between start and end images with fast high-quality results. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.1per run·~10 / $1
Camera spins, fabrics swirl, colors morph into a new outfit.
A time-lapse sequence showing the sun arcing across the sky, shadows moving dynamically across the mountain face, and the fog in the valley slowly dissipating to reveal a lush green landscape.
Her expression slowly and subtly transforms. The sadness in her eyes begins to fade, replaced by a growing spark of determination. A faint, hopeful smile begins to form on her lips as she lifts her gaze, looking directly into the camera.
Gentle wind blows, petals swirl, the temperature drops as the scene gradually turns cold.
Lights flicker as the music builds, her reflection in the window begins to change.
The camera zooms into the painting as her reflection merges with the artwork.
The character is running, and eventually, they slowly grow up.
The dream collapses, clouds dissolve into dust, fading into a real bedroom.
Seasons whirl — the ice melts, green life bursts forth.
Vidu Q2 Start-End to Video Turbo creates a coherent video from just two images: a start frame and an end frame. Turbo accelerates bi-frame interpolation with optimized inference for faster turnaround and smoother motion — ideal for scene transitions, bridging shots, and rapid storytelling.
Bi-frame guidance Anchors identity, layout, and lighting from both start and end frames.
Turbo temporal smoothing Reduces flicker and popping while preserving subject integrity at higher throughput.
Object and human-aware motion Protects faces, hands, hair, and thin structures while animating garments and props.
Layout-smart interpolation Respects depth, occlusion, and parallax for clean foreground/background motion.
Adaptive camera pathing Natural pans, push-ins, and dolly moves without warping.
Speed-optimized pipeline Faster than Pro at the same settings, perfect for rapid iteration.
| Parameter | Required | Description |
|---|---|---|
| start_frame | Yes | Starting image for the video |
| end_frame | Yes | Ending image for the video |
| prompt | No | Describe motion style, mood, or camera movement |
| duration | No | Video length in seconds (1–10) |
| resolution | No | Output resolution: 540p, 720p, or 1080p |
| movement_amplitude | No | Motion intensity: auto, small, medium, or large |
| bgm | No | Enable background music generation |
| seed | No | Random seed for reproducibility (-1 for random) |
| Resolution | Duration | Price |
|---|---|---|
| 540p | 1s | $0.03 |
| 540p | 2s | $0.04 |
| 540p | 3s | $0.05 |
| 540p | 4s | $0.06 |
| 540p | 5s | $0.07 |
| 540p | 6s | $0.08 |
| 540p | 7s | $0.09 |
| 540p | 8s | $0.10 |
| 540p | 9s | $0.20 |
| 540p | 10s | $0.30 |
| 720p | 1s | $0.04 |
| 720p | 2s | $0.05 |
| 720p | 3s | $0.10 |
| 720p | 4s | $0.15 |
| 720p | 5s | $0.20 |
| 720p | 6s | $0.25 |
| 720p | 7s | $0.30 |
| 720p | 8s | $0.35 |
| 720p | 9s | $0.45 |
| 720p | 10s | $0.50 |
| 1080p | 1s | $0.175 |
| 1080p | 2s | $0.225 |
| 1080p | 3s | $0.275 |
| 1080p | 4s | $0.325 |
| 1080p | 5s | $0.375 |
| 1080p | 6s | $0.425 |
| 1080p | 7s | $0.475 |
| 1080p | 8s | $0.525 |
| 1080p | 9s | $0.625 |
| 1080p | 10s | $0.725 |
540p: $0.03 for 1s, +$0.01/s up to 8s, then $0.20 for 9s, $0.30 for 10s
720p: $0.04 for 1s, $0.05 for 2s, then +$0.05/s from 3s
1080p: $0.175 for 1s, then +$0.05/s up to 8s, then +$0.10/s for 9s-10s
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-q2-turbo with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Start End To Video Q2 Turbo below.
set -euo pipefail
: "${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-turbo" \
-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-turbo";
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-turbo", 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 Turbo is a Vidu model for video generation from images, exposed as a REST API on WaveSpeedAI. Vidu Q2 Turbo Start-End to Video creates smooth Image-to-Video transitions between start and end images with fast high-quality results. 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-turbo.
Start End To Video Q2 Turbo starts at $0.10 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-turbo.
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.