Alibaba HappyHorse 1.1 Video Extend extends existing videos with seamless AI-generated continuation, supporting 720P / 1080P output while preserving visual continuity and motion consistency. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
就绪
$0.7每次运行·~14 / $10
A black cat wearing a tiny detective collar, sitting at the entrance of a narrow London alley, with wet cobblestones, warm window light, fog, a cinematic mystery atmosphere, realistic animal photography.
Alibaba Happy Horse 1.1 Video Extend continues an existing video clip with new AI-generated footage, seamlessly extending the scene from where the original ends. Upload a source video, describe how the video should continue, and generate a coherent continuation in 720p or 1080p.
Seamless video continuation
Extend an existing clip with newly generated footage that picks up naturally from the original ending.
Prompt-guided scene progression
Describe how the action, camera movement, atmosphere, or story should continue using natural language.
Cinematic motion
Generate smooth, expressive motion while preserving the overall feel and flow of the source video.
Flexible resolution options
Choose 720p for lower-cost iteration or 1080p for higher-resolution output.
Production-ready API
Access the model through a REST inference API for scalable integration into creative workflows.
| Parameter | Required | Description |
|---|---|---|
| video | Yes | Source video to extend. |
| prompt | Yes | Text description of the desired continuation. |
| resolution | No | Output resolution: 720p default or 1080p. |
| duration | No | Total output duration in seconds. Range: 3–15, default 5. |
| seed | No | Random seed for reproducibility. Range: 0–2147483647. |
720p for lower-cost iteration or 1080p for higher-resolution output.3 and 15 seconds.Continue the scene with a slow forward camera movement, the subject walking deeper into the alley, soft rain beginning to fall, stronger reflections on the ground, and a cinematic moody nighttime atmosphere.
| Resolution | Cost |
|---|---|
| 720p | $0.70 |
| 1080p | $0.945 |
| Resolution | Cost |
|---|---|
| 720p | $0.14 |
| 1080p | $0.189 |
| Resolution | 3s | 5s | 10s | 15s |
|---|---|---|---|---|
| 720p | $0.42 | $0.70 | $1.40 | $2.10 |
| 1080p | $0.567 | $0.945 | $1.89 | $2.835 |
720p for quick testing, then switch to 1080p for higher-resolution output.seed when you want more reproducible generations.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/happyhorse-1.1/video-extend with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Happyhorse 1.1 Video Extend below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"resolution": "720p",
"duration": 5
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/alibaba/happyhorse-1.1/video-extend" \
-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/alibaba/happyhorse-1.1/video-extend";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"resolution": "720p",
"duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"resolution": "720p",
"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/alibaba/happyhorse-1.1/video-extend", 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)Happyhorse 1.1 Video Extend is a Alibaba model for video extension, exposed as a REST API on WaveSpeedAI. Alibaba HappyHorse 1.1 Video Extend extends existing videos with seamless AI-generated continuation, supporting 720P / 1080P output while preserving visual continuity and motion consistency. 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/alibaba/alibaba-happyhorse-1.1-video-extend.
Happyhorse 1.1 Video Extend starts at $0.70 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`, `video`, `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/alibaba/alibaba-happyhorse-1.1-video-extend.
Median end-to-end generation time on WaveSpeedAI is around 226 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 (Alibaba). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.