Wan 3.0 Image to Video animates a first-frame image into a cinematic video, with optional last-frame guidance, flexible 2-30 second duration, aspect ratio control, optional audio, and deep-thinking controls for high-quality motion and scene continuity. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.6per run·~16 / $10
The dragon slowly opens its eyes, leaves move from its breathing, glowing particles float around the forest, the ranger slowly steps forward and reaches out a hand. The camera slowly circles around both characters revealing the enormous scale difference.
Wan 3.0 Image-to-Video animates a first-frame image into a coherent video with optional last-frame guidance, audio generation, and deep-thinking mode. It preserves the input image's subject and composition while adding prompt-guided motion, camera movement, and scene progression.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | First-frame image URL or Base64-encoded data. |
| last_image | No | Optional last-frame image used to guide the ending of the video. |
| prompt | Yes | Text prompt describing the desired motion, action, scene, camera movement, lighting, and style. |
| resolution | No | Output resolution: 480p, 720p, or 1080p. Default: 720p. |
| aspect_ratio | No | Output aspect ratio. If omitted, the output adapts to the input image. |
| duration | No | Output duration in seconds. Range: 2–30. Default: 5. |
| thinking_mode | No | Enable deep-thinking mode for more deliberate prompt interpretation. Default: false. |
| enable_audio | No | Include audio in the output. Default: true. |
| seed | No | Random seed from 0 to 2147483647. |
last_image when you want to control the final frame or ending direction.480p for lower-cost drafts, 720p for balanced output, or 1080p for higher quality.2 to 30 seconds.enable_audio enabled when audio is needed.thinking_mode for more complex motion or composition instructions.Pricing is based on output resolution and billed duration.
Billed duration is rounded up to the next whole second and clamped to the 2–30s range.
| Resolution | Per 5s | Per second |
|---|---|---|
| 480p | $0.35 | $0.07 |
| 720p | $0.65 | $0.13 |
| 1080p | $1.40 | $0.28 |
| Resolution | 2s | 5s | 10s | 30s |
|---|---|---|---|---|
| 480p | $0.14 | $0.35 | $0.70 | $2.10 |
| 720p | $0.26 | $0.65 | $1.30 | $3.90 |
| 1080p | $0.56 | $1.40 | $2.80 | $8.40 |
image and last_image to guide both the beginning and ending of the video.last_image when the final pose, composition, or ending frame matters.480p for quick drafts and 1080p for higher-quality output.thinking_mode for complex prompts with multiple movement or composition requirements.seed when you want more reproducible results.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/wan-3.0/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Wan 3.0 Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"aspect_ratio": "16:9",
"duration": 5,
"thinking_mode": false,
"enable_audio": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/alibaba/wan-3.0/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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/alibaba/wan-3.0/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"aspect_ratio": "16:9",
"duration": 5,
"thinking_mode": false,
"enable_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"resolution": "720p",
"aspect_ratio": "16:9",
"duration": 5,
"thinking_mode": False,
"enable_audio": True
}
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-3.0/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 3.0 Image To Video is a Alibaba model for video generation from images, exposed as a REST API on WaveSpeedAI. Wan 3.0 Image to Video animates a first-frame image into a cinematic video, with optional last-frame guidance, flexible 2-30 second duration, aspect ratio control, optional audio, and deep-thinking controls for high-quality motion and scene continuity. 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-wan-3.0-image-to-video.
Wan 3.0 Image To Video starts at $0.60 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`, `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-3.0-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 10488 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.