Alibaba HappyHorse 1.1 Text-to-Video generates cinematic 720P or 1080P videos from text prompts, with smooth camera movement, expressive motion, and strong prompt fidelity. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Siap
$0.7per run·~14 / $10
A luxury perfume bottle standing on black marble, surrounded by white roses, soft mist, silver reflections, elegant high-end commercial photography, dramatic spotlight. Mist rolls across the marble surface as rose petals fall around the perfume bottle. A beam of light sweeps over the glass, revealing reflections and liquid movement inside the bottle. Smooth luxury product commercial motion.
Alibaba Happy Horse 1.1 Text-to-Video turns natural-language prompts into cinematic videos with smooth motion, strong prompt alignment, and stable subject rendering. It supports both 720p and 1080p output, along with multiple aspect ratios for ad creatives, social content, storytelling, and concept work.
Strong prompt fidelity
Follows detailed instructions for scene composition, action, lighting, mood, and camera movement.
Cinematic motion
Generates smooth, expressive motion with stable subjects and polished visual dynamics.
Flexible aspect ratios
Supports 16:9, 9:16, 1:1, 4:3, and 3:4 for landscape, portrait, and square video formats.
Resolution options for different needs
Choose 720p for lower-cost iteration or 1080p for higher-resolution outputs.
Production-ready API
Access the model through a REST inference API for easy integration into creative workflows.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text prompt describing the desired video. Maximum 2500 characters. |
| aspect_ratio | No | Output aspect ratio: 16:9 default, 9:16, 1:1, 4:3, or 3:4. |
| resolution | No | Output resolution: 720p default or 1080p. |
| duration | No | Video length 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.A cinematic street scene at night, light rain falling, soft reflections on wet pavement, a stylish woman walking slowly toward the camera, gentle dolly-in movement, neon glow, shallow depth of field, elegant and atmospheric mood.
| 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 | $2.835 |
720p for rapid testing, then switch to 1080p for higher-resolution outputs.9:16 for short-form mobile, 16:9 for widescreen, and 1:1 for square layouts.seed when you want more reproducible outputs.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/happyhorse-1.1/text-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Happyhorse 1.1 Text To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/alibaba/happyhorse-1.1/text-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/alibaba/happyhorse-1.1/text-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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"aspect_ratio": "16:9",
"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/text-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)Happyhorse 1.1 Text To Video is a Alibaba model for video generation, exposed as a REST API on WaveSpeedAI. Alibaba HappyHorse 1.1 Text-to-Video generates cinematic 720P or 1080P videos from text prompts, with smooth camera movement, expressive motion, and strong prompt fidelity. 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-text-to-video.
Happyhorse 1.1 Text To Video 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`, `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/alibaba/alibaba-happyhorse-1.1-text-to-video.
Median end-to-end generation time on WaveSpeedAI is around 162 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.