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Wan 3.0 Prime Reference to Video is an accelerated variant that creates coherent videos from prompts and multimodal references, including images, videos, and audio, with flexible 2-30 second duration and aspect ratio control for subject consistency, motion guidance, timing control, and scene continuity. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-video
Input

Idle

$0.75per run·~13 / $10

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ExamplesView all

A cinematic science-fiction scene inside a colossal orbital scrapyard above a distant planet. The silver-haired female navigator walks carefully through rows of abandoned spacecraft while holding the transparent star-map device. The black-coated synthetic operative waits beside a damaged shuttle, holding the floating black sphere above his mechanical hand. She stops several steps away from him and activates the star map. Cyan holographic constellations expand between them. The man raises the black sphere, and its geometric shell slowly opens, revealing a small intensely glowing energy core. Both characters suddenly look upward as the entire scrapyard loses power. The surrounding machinery shuts down and distant warning lights turn red. The camera begins with a wide shot revealing the enormous orbital scrapyard, slowly tracks beside the female navigator as she approaches, then settles into a low cinematic two-shot during the exchange. End with both characters illuminated only by the cyan star map and the orange energy core. Maintain the exact identities, hairstyles, facial features, outfits, equipment, colors, and proportions of both reference characters. Realistic restrained acting, slow atmospheric camera movement, industrial science-fiction production design, cinematic volumetric lighting, subtle zero-gravity debris, high-end sci-fi movie aesthetic.

Related Models

README

Wan 3.0 Prime Reference-to-Video

Wan 3.0 Prime Reference-to-Video combines reference images, videos, and audio with a prompt to create coherent video scenes. It supports multimodal reference guidance for character consistency, object control, motion direction, audio style, and scene composition.

Parameters

ParameterRequiredDescription
promptYesText prompt describing the desired scene, subject, motion, camera movement, lighting, and style.
reference_imagesConditionalUp to 10 reference images. At least one reference media array is required.
reference_videosConditionalUp to 5 reference video inputs. Each video must be MP4 or MOV, 1–15 seconds, 240–4096 pixels on each side, no more than 8:1 aspect ratio, and no more than 100 MB. Total reference video duration must not exceed 15 seconds.
reference_audiosConditionalUp to 5 reference audio files. Total reference audio duration must not exceed 15 seconds.
resolutionNoOutput resolution: 480p, 720p, or 1080p. Default: 720p.
aspect_ratioNoOutput aspect ratio. Default: 16:9.
durationNoGenerated video duration in seconds. Default: 5. Without video input: integer 2–30. With reference videos: total input video duration + output video duration must not exceed 30s.
thinking_modeNoEnable deep-thinking mode for more deliberate prompt interpretation. Default: false.
enable_audioNoInclude audio in the output. Default: true.
seedNoRandom seed from 0 to 2147483647.

At least one of reference_images, reference_videos, or reference_audios is required.

How to Use

  1. Add reference media — Provide reference images, videos, audio, or a combination of them.
  2. Write your prompt — Describe the target scene, action, camera movement, lighting, style, and how the references should be used.
  3. Choose resolution — Use 480p for lower-cost drafts, 720p for balanced output, or 1080p for higher quality.
  4. Set aspect ratio — Select the output format that matches your target platform or creative direction.
  5. Set duration — Choose a duration from 2 to 30 seconds.
  6. Configure audio optional — Keep enable_audio enabled when audio is needed.
  7. Enable thinking mode optional — Use thinking_mode for complex prompts with multiple reference requirements.
  8. Submit — Generate the final reference-guided video.

Pricing

Wan 3.0 Prime Reference-to-Video is priced at 1.5× the corresponding non-Prime model. Pricing uses normalized reference-video duration plus output duration. Each reference video is padded to at least 1 second, capped at 15 seconds, and the combined input is capped at 15 seconds and rounded up to a whole second for billing.

ResolutionPer secondPer 5s
480p$0.075$0.375
720p$0.15$0.75
1080p$0.30$1.50

Example: 5 seconds of normalized input plus 5 seconds of output costs $0.75 at 480p, $1.50 at 720p, or $3.00 at 1080p.

Best Use Cases

  • Character-consistent storytelling — Generate scenes guided by character, face, outfit, or style references.
  • Multi-reference video generation — Combine people, objects, environments, motion, and audio references in one workflow.
  • Product and brand videos — Use reference assets to preserve product appearance or campaign style.
  • Audio-guided scenes — Use reference audio to guide mood, rhythm, ambience, or sound direction.
  • Social media content — Create short-form videos from multimodal references.
  • Creative prototyping — Test different scene directions while keeping reference-guided consistency.

Pro Tips

  • Use clear reference images when identity, product detail, or visual style matters.
  • Use reference videos when motion, pacing, gesture, or camera behavior matters.
  • Use reference audio when ambience, rhythm, voice style, or soundtrack direction matters.
  • Explain how each reference should influence the final video in the prompt.
  • Use 480p for quick drafts and 1080p for higher-quality output.
  • Enable thinking_mode for prompts that combine multiple references or detailed scene requirements.
  • Set a fixed seed when you want more reproducible results.

Related Models

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Wan 3.0 Prime Reference To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/wan-3.0-prime/reference-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 Prime Reference 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,
    "thinking_mode": 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-3.0-prime/reference-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-3.0-prime/reference-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "resolution": "720p",
        "aspect_ratio": "16:9",
        "duration": 5,
        "thinking_mode": false,
        "enable_audio": true,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
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,
    "thinking_mode": 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-3.0-prime/reference-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 Prime Reference To Video API — Frequently asked questions

What is the Wan 3.0 Prime Reference To Video API?

Wan 3.0 Prime Reference To Video is a Alibaba model for video generation from images, exposed as a REST API on WaveSpeedAI. Wan 3.0 Prime Reference to Video is an accelerated variant that creates coherent videos from prompts and multimodal references, including images, videos, and audio, with flexible 2-30 second duration and aspect ratio control for subject consistency, motion guidance, timing control, 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.

How do I call the Wan 3.0 Prime Reference 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-3.0-prime-reference-to-video.

How much does Wan 3.0 Prime Reference To Video cost per run?

Wan 3.0 Prime Reference To Video starts at $0.75 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 3.0 Prime Reference To Video accept?

Key inputs: `prompt`, `aspect_ratio`, `resolution`, `duration`, `seed`, `reference_images`. 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-prime-reference-to-video.

How do I get started with the Wan 3.0 Prime Reference To Video API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use Wan 3.0 Prime Reference 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 3.0 Prime Reference to Video API on WaveSpeedAI