WAN 2.6 Flash converts images into videos (720p/1080p) with optional audio, optimized for speed and cost. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.125실행당·~80 / $10
The runner continues jogging forward with subtle arm swing and steady cadence. Strong wind pushes the runner’s hair and jacket fabric. Street lamps on the right glow warmly with soft bokeh and slight streaking. The ocean on the left surges with waves and mist. Camera: handheld, low-to-mid height, tracking alongside the runner, slight micro-shake, shallow depth of field, natural motion blur.
A human figure shown from the waist up, facing forward, with a body made of transparent glass. Inside the glass body, there are only a realistic human brain and a heart, both colored in bright neon lime green. No blood vessels, no skeleton, no other organs. Everything is contained inside the glass body. A white seamless studio background, soft professional studio lighting, clean shadows, a minimalist and calm composition. It looks like a real studio photograph.
A close-up video clip based on the provided image, showing the pair of gold hoop earrings with pave diamonds and teardrop pearl drops resting on the beige silk fabric. The earrings gently sway and shift slightly, as if caught in a soft, slow breeze. The movement is subtle and natural. The silk fabric folds also ripple and move slowly. The lighting is soft, diffused, and natural, creating a matte finish on the gold and a gentle luster on the pearls without any harsh glares, sparkles, or bright reflections. The camera remains steady, focused on the moving earrings.
a man carrying a tall flower and running down
i want to create a image of elegant woman unfurling an hermes scarf
Wan 2.6 Image-to-Video Flash is a fast image-to-video generation model from. Upload an image, describe the motion you want, and generate videos up to 15 seconds with optional synchronized audio — all with quick turnaround and flexible pricing.
Image-driven video generation Transform static images into dynamic videos with AI-generated motion.
Optional audio generation Generate videos with synchronized audio or output silent videos.
Custom audio input Optionally upload your own audio to sync with the generated video.
Multiple shot types Choose single or multi-shot modes for different creative needs.
Prompt Enhancer Built-in tool to automatically optimize your prompts for better results.
Fast generation Flash variant optimized for quick turnaround.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source image to animate (upload or URL) |
| prompt | Yes | Describe the motion and action you want |
| audio | No | Custom audio file to sync with video |
| negative_prompt | No | Describe what to avoid in the output |
| resolution | No | Output resolution: 720p or 1080p (default: 720p) |
| duration | No | Video length in seconds, up to 15 (default: 15) |
| shot_type | No | Shot mode: single or multi (default: single) |
| enable_prompt_expansion | No | Enable prompt optimizer for better results |
| enable_audio | No | Output video with audio (default: true) |
| seed | No | Random seed for reproducibility (-1 for random) |
| Mode | Description |
|---|---|
| single | Single continuous shot |
| multi | Multiple shots with scene transitions |
| Resolution | Audio | Cost per 5 seconds |
|---|---|---|
| 720p | Off | $0.125 |
| 720p | On | $0.25 |
| 1080p | Off | $0.1875 |
| 1080p | On | $0.375 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/wan-2.6/image-to-video-flash with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Wan 2.6 Image To Video Flash below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"duration": 5,
"shot_type": "single",
"enable_prompt_expansion": false,
"enable_audio": true,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/alibaba/wan-2.6/image-to-video-flash" \
-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-2.6/image-to-video-flash";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"duration": 5,
"shot_type": "single",
"enable_prompt_expansion": false,
"enable_audio": true,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"duration": 5,
"shot_type": "single",
"enable_prompt_expansion": False,
"enable_audio": True,
"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-2.6/image-to-video-flash", 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 2.6 Image To Video Flash is a Alibaba model for video generation from images, exposed as a REST API on WaveSpeedAI. WAN 2.6 Flash converts images into videos (720p/1080p) with optional audio, optimized for speed and cost. 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-2.6-image-to-video-flash.
Wan 2.6 Image To Video Flash starts at $0.13 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`, `audio`, `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-2.6-image-to-video-flash.
Median end-to-end generation time on WaveSpeedAI is around 34 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.