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alibaba/

WAN 2.7 Text-to-Video turns plain prompts into coherent, cinematic clips with crisp detail, stable motion, and strong instruction-following—great for ads, explainers, and social posts. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

text-to-video
इनपुट

निष्क्रिय

$0.5प्रति रन·~20 / $10

आगे:

उदाहरणसभी देखें

Close-up portrait of a woman pressing her palm against a rain-soaked window at night, shot from outside. Heavy rain streaks distort her face through the glass, neon signs from the street below cast fragmented magenta and cyan reflections across her skin. Camera slowly pushes in at 0.3x speed. Shallow depth of field, bokeh rain drops, dual-exposure ghosting of her reflection overlapping her face. Cinematic 4K, anamorphic lens flare, 24fps.

Extreme close-up portrait, woman's face in profile against golden hour sun, shot with 85mm f/1.2. Sun positioned directly behind her head creating a blazing rim light halo. Individual strands of hair catch the light and glow translucent amber. A gentle breeze causes hair to drift across her cheek in slow motion. Skin subsurface scattering visible on ear and nose tip. Dust particles float through the backlit air. Hyper-realistic, 120fps slow motion playback at 24fps, anamorphic 2.39:1.

Portrait of a woman aging from 20 to 80 and back to 20, seamless morphing over 10 seconds. Shot in style of 1960s 16mm film with authentic gate weave, film grain, occasional splice marks and light leaks. The background remains constant — a sun-drenched window in a Paris apartment — while only her face transforms. Color grade shifts from saturated Kodachrome warmth in youth to desaturated, slightly faded tones in age, then back. No CGI aesthetic — must feel like archival film footage.

संबंधित मॉडल

README

Wan 2.7 Text-to-Video

Wan 2.7 is advanced text-to-video model, generating high-quality cinematic video from natural language prompts. With audio input support, negative prompt control, flexible resolution and aspect ratio options, and an optional prompt expansion mode, it delivers strong results for a wide range of creative and production workflows.

Why Choose This?

  • High-quality text-to-video generation Produces detailed, visually coherent video with accurate motion, lighting, and scene composition from text descriptions.

  • Audio input support Upload an audio track to guide the rhythm, mood, and pacing of the generated video for synchronized results.

  • Negative prompt support Specify what you don't want in the video for more precise control over the output.

  • Prompt expansion Enable enable_prompt_expansion to let the model automatically enrich and optimize your prompt before generation.

  • Resolution options Generate at 720p or 1080p to match your delivery requirements.

  • Flexible aspect ratios Supports multiple orientations for social, cinematic, and broadcast formats.

  • Reproducible results Use the seed parameter to lock in a specific output for exact reproduction.

Parameters

ParameterRequiredDescription
promptYesText description of the scene, motion, camera style, and atmosphere.
negative_promptNoElements to exclude from the generated video.
audioNoOptional audio track to synchronize with the generated video.
resolutionNoOutput resolution: 720p (default) or 1080p.
aspect_ratioNoOutput aspect ratio. Default: 16:9.
durationNoClip length in seconds. Default: 5.
enable_prompt_expansionNoEnable automatic prompt optimization before generation. Default: off.
seedNoRandom seed for reproducible results. Use -1 for a random seed.

How to Use

  1. Write your prompt — describe the scene, characters, camera movement, lighting, and atmosphere. Use the Prompt Enhancer for better results.
  2. Add negative prompt (optional) — specify elements you want to exclude from the output.
  3. Upload audio (optional) — provide an audio file or URL to synchronize the video to a specific track.
  4. Select resolution — 720p for standard output, 1080p for higher-quality results.
  5. Select aspect ratio — choose the format that fits your target platform.
  6. Set duration — choose your desired clip length in seconds.
  7. Enable prompt expansion (optional) — let the model automatically enrich your prompt before generation.
  8. Set seed (optional) — fix the seed to reproduce a specific result in future runs.
  9. Submit — generate, preview, and download your video.

Pricing

Duration720p1080p
5s$0.50$0.75
10s$1.00$1.50
15s$1.50$2.25

Billing Rules

  • 720p: $0.10 per second
  • 1080p: $0.15 per second (1.5× base rate)

Best Use Cases

  • Cinematic Storytelling — Render atmospheric, narrative-driven scenes from detailed text descriptions.
  • Social Media Content — Generate platform-optimized video clips across multiple aspect ratios.
  • Marketing & Advertising — Produce high-quality promotional video content without a film crew.
  • Music & Audio-Visual — Synchronize generated video to a music track or voiceover for cohesive results.
  • Concept Visualization — Bring creative ideas and moods to life quickly for pitching and review.

Pro Tips

  • The more specific your prompt, the better — include camera angle, lighting style, color palette, and subject behavior.
  • Use negative_prompt to avoid common artifacts like blurry faces or unwanted motion.
  • Enable prompt expansion for shorter or less detailed prompts to get richer output automatically.
  • Providing an audio track improves rhythm and pacing alignment in the generated video.
  • Fix the seed once you find a result you like to iterate consistently across resolution and duration changes.

Notes

  • Only prompt is required; all other parameters are optional.
  • Ensure audio URLs are publicly accessible if using a link rather than a direct upload.
  • Please ensure your content complies with usage policies.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है। दस्तावेज़ की कीमतें केवल संदर्भ के लिए हैं और पुरानी हो सकती हैं। Generate बटन अनुमान दिखाता है; टास्क का अंतिम शुल्क ही मान्य होगा।

Wan 2.7 Text To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/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 Wan 2.7 Text To Video below.

HTTP example
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",
    "resolution": "720p",
    "aspect_ratio": "16:9",
    "duration": 5,
    "enable_prompt_expansion": false,
    "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "resolution": "720p",
        "aspect_ratio": "16:9",
        "duration": 5,
        "enable_prompt_expansion": false,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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",
    "resolution": "720p",
    "aspect_ratio": "16:9",
    "duration": 5,
    "enable_prompt_expansion": False,
    "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/alibaba/wan-2.7/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)

Wan 2.7 Text To Video API — Frequently asked questions

What is the Wan 2.7 Text To Video API?

Wan 2.7 Text To Video is a Alibaba model for video generation, exposed as a REST API on WaveSpeedAI. WAN 2.7 Text-to-Video turns plain prompts into coherent, cinematic clips with crisp detail, stable motion, and strong instruction-following—great for ads, explainers, and social posts. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Wan 2.7 Text To Video API?

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-wan-2.7-text-to-video.

How much does Wan 2.7 Text To Video cost per run?

Wan 2.7 Text To Video starts at $0.50 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.

What inputs does Wan 2.7 Text To Video accept?

Key inputs: `prompt`, `audio`, `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-wan-2.7-text-to-video.

How long does Wan 2.7 Text To Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 87 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Wan 2.7 Text To Video outputs commercially?

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.

Wan 2.7 Text to Video | Powerful Text-to-Video API on WaveSpeedAI