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Wan 2.2 Image-to-Video turns a single image into smooth, cinematic motion with clean detail—ideal for storyboards, mood shots, and product demos. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-video
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就緒

$0.15每次運行·~66 / $10

下一步:

示例查看全部

A person walking through a corridor of shattered clocks and frozen time fragments. Motion blur trails, glowing fragments suspended midair, surreal lighting, concept art style.

A futuristic astronaut ascends toward a massive sun halo, golden plasma arcs swirling around. The suit reflects molten light, cinematic wide shot, extreme contrast, ultra-detailed

A girl floats peacefully underwater surrounded by glowing jellyfish and beams of refracted sunlight. Her hair drifts like silk, surreal calmness, ultra-realistic detail, dreamlike tone

A queen of ice stands on a cliff of crystal glaciers, northern lights dancing above—flowing icy gown, reflective surfaces, ethereal atmosphere, detailed environment design.

A traveler walks through a vast desert of red dunes at sunset, carrying a glowing orb. Wind lifts sand into spirals, long shadows, epic cinematic composition, volumetric lighting

A person walking quickly through a corridor of shattered clocks and frozen time fragments, fragments flying slightly around with natural motion, no slow motion, dynamic pacing, cinematic lighting, concept art style.

相關模型

README

Wan 2.2 — Image-to-Video (I2V)

Wan 2.2 is a next-gen I2V model built on a Mixture-of-Experts denoising architecture. It turns a single still image into a smooth, cinematic short video with strong prompt adherence and stable motion.

Why it looks great

  • Film-grade control: Understands lighting, color, composition, and camera language for cohesive scenes.
  • Stable large motion: Handles fast subject/camera movement with fewer jitters or tears.
  • Accurate semantics: Follows detailed prompts in complex, multi-object scenes.
  • Pure I2V workflow: No start/end keyframes required—one reference image is enough.

Inputs & Parameters

  • image (required): Reference image to lock identity, layout, and style.
  • prompt (required): Scene mood, motion, and camera cues (e.g., “slow dolly-in, warm rim light”).
  • negative_prompt (optional): Things to avoid (e.g., “text, watermark, distortion”).
  • size: 832×480 or 1280×720.
  • duration: 5 s or 8 s.
  • seed: Integer; fixed for reproducibility, −1 for random.
  • last_image (optional): The last frame of the video.

How to Use

  1. Upload the image.
  2. Add a concise prompt (subject + environment + motion + lighting).
  3. Choose size (480p/720p) and duration (5 s/8 s).
  4. (Optional) Set negative_prompt and seed.
  5. Run and download.

Pricing

Duration832×480 (480p)1280×720 (720p)
5 s$0.15$0.30
8 s$0.24$0.48
提示:本網站部分功能由第三方 AI 模型提供支援。文件價格僅供參考,可能已過時。Generate 按鈕顯示預估價格,最終以任務實際收費為準。

Wan 2.2 Image To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/image-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 2.2 Image 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",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "resolution": "480p",
    "duration": 5,
    "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/image-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/wavespeed-ai/wan-2.2/image-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "resolution": "480p",
        "duration": 5,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "resolution": "480p",
    "duration": 5,
    "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/wavespeed-ai/wan-2.2/image-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 2.2 Image To Video API — Frequently asked questions

What is the Wan 2.2 Image To Video API?

Wan 2.2 Image To Video is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. Wan 2.2 Image-to-Video turns a single image into smooth, cinematic motion with clean detail—ideal for storyboards, mood shots, and product demos. 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 2.2 Image 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/wavespeed-ai/wan-2.2-image-to-video.

How much does Wan 2.2 Image To Video cost per run?

Wan 2.2 Image To Video 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.

What inputs does Wan 2.2 Image To Video accept?

Key inputs: `prompt`, `image`, `resolution`, `duration`, `seed`, `negative_prompt`. 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/wavespeed-ai/wan-2.2-image-to-video.

How long does Wan 2.2 Image To Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 57 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 2.2 Image To Video outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Wan 2.2 Image to Video | Fast Image-to-Video API on WaveSpeedAI