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Wan 2.1 14B Vace

wavespeed-ai /

WAN 2.1 VACE is an all-in-one video model supporting Reference-to-Video (Image-to-Video), V2V, Masked V2V and Move/Swap/Animate capabilities. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-video
입력

대기 중

$0.3실행당·~33 / $10

다음:

예시전체 보기

The girl showed a brilliant smile.

The elegant lady carefully selects bags.

Santa Claus in front of the Christmas tree.

Bees carefully gather nectar.

The dragon spits fire at the castle.

The elegant lady carefully selects bags in the boutique, and she shows the charm of a mature woman in a black slim dress with a pearl necklace, as well as her pretty face. Holding a vintage-inspired blue leather half-moon handbag, she is carefully observing its craftsmanship and texture. The interior of the store is a haven of sophistication and luxury. Soft, ambient lighting casts a warm glow over the polished wooden floors

The girl is holding a bouquet of flowers.

The girl is holding a doll.

The girl is holding a cabbage doll.

관련 모델

README

Wan 2.1 14B VACE — wavespeed-ai/wan-2.1-14b-vace

Wan 2.1 14B VACE is a versatile, production-oriented video generation and editing model that supports multi-input workflows. You can provide a text prompt plus up to 5 reference images, and optionally add a source video, masks, or start/end frames to guide motion, structure, and edits. It also includes multiple task modes (e.g., depth) for more controlled video understanding and generation.

Key capabilities

  • Prompt-driven video generation with multi-modal controls
  • Up to 5 reference images to guide identity, style, wardrobe, or scene details
  • Optional video input for video-to-video transformation workflows
  • Mask support (mask_video / mask_image) for region-based edits
  • First/last frame guidance (first_image / last_image) for better continuity
  • Task modes (e.g., depth) for structured control and more predictable results

Use cases

  • Reference-guided video generation (character/style consistency across shots)
  • Video editing with masks (replace background, remove objects, localized changes)
  • Start-to-end guided storytelling using first_image + last_image
  • Video-to-video restyling (apply a new look while keeping motion)
  • Controlled motion and composition using task settings (e.g., depth)

Pricing

ModeSizePrice per 5s video
Standard832×480$0.30
Fast Mode832×480$0.15
Standard1280×720 / 720×1280$0.40
Fast Mode1280×720 / 720×1280$0.25

Longer durations are billed in steps based on duration.

Inputs

  • prompt (required): what should happen in the video
  • images (optional): up to 5 reference images
  • video (optional): source video for video-to-video workflows
  • mask_video (optional): video mask for localized video edits
  • mask_image (optional): image mask for localized edits
  • first_image (optional): starting frame guidance
  • last_image (optional): ending frame guidance
  • negative_prompt (optional): what to avoid

Parameters

  • task: control mode selector (e.g., depth)
  • duration: video length (e.g., 5s)
  • size: output resolution (e.g., 832×480, 1280×720)
  • num_inference_steps: sampling steps
  • guidance_scale: prompt adherence strength
  • flow_shift: motion/flow behavior tuning
  • context_scale: reference/context strength tuning
  • seed: random seed (-1 for random; fixed for reproducibility)
  • enable_fast_mode: speed-optimized mode (if available in your UI)

Prompting guide (multi-reference + optional masks)

A reliable structure:

  1. Define the main subject and action
  2. Specify environment and camera beats
  3. Assign roles to references (identity/style/outfit/background)
  4. If using masks, clearly state what changes inside vs. outside the mask
  5. If using first/last frames, describe how the motion should transition between them

Template: Use image 1 for identity, image 2 for outfit, image 3 for style. Generate a 5-second clip where [action]. Keep identity consistent. If mask is provided, change only the masked region to [edit], keep everything else unchanged.

Example prompts

  • An elegant lady carefully selects bags in a boutique. Soft natural lighting, shallow depth of field, subtle camera push-in, gentle hand movements, realistic fabric and leather textures.
  • Use the reference images for the same character and outfit. Walk through a luxury store aisle, turn to examine a handbag, warm highlights on leather, calm cinematic pacing.
  • If mask is provided: Replace only the masked background with a modern boutique interior, keep the subject unchanged, match lighting and shadows.
참고:이 웹사이트는 제3자가 제공하는 AI 모델을 사용합니다.

Wan 2.1 14b Vace API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1-14b-vace 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 2.1 14b Vace 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",
    "task": "depth",
    "duration": 5,
    "size": "832*480",
    "num_inference_steps": 30,
    "guidance_scale": 5,
    "flow_shift": 16,
    "context_scale": 1,
    "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-2.1-14b-vace" \
  -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-2.1-14b-vace";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "task": "depth",
        "duration": 5,
        "size": "832*480",
        "num_inference_steps": 30,
        "guidance_scale": 5,
        "flow_shift": 16,
        "context_scale": 1,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "task": "depth",
    "duration": 5,
    "size": "832*480",
    "num_inference_steps": 30,
    "guidance_scale": 5,
    "flow_shift": 16,
    "context_scale": 1,
    "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-2.1-14b-vace", 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 2.1 14b Vace API — Frequently asked questions

What is the Wan 2.1 14b Vace API?

Wan 2.1 14b Vace is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. WAN 2.1 VACE is an all-in-one video model supporting Reference-to-Video (Image-to-Video), V2V, Masked V2V and Move/Swap/Animate capabilities. 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 2.1 14b Vace 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-2.1-14b-vace.

How much does Wan 2.1 14b Vace cost per run?

Wan 2.1 14b Vace starts at $0.30 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 2.1 14b Vace accept?

Key inputs: `prompt`, `images`, `video`, `duration`, `size`, `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/wavespeed-ai/wan-2.1-14b-vace.

How long does Wan 2.1 14b Vace take to generate?

Median end-to-end generation time on WaveSpeedAI is around 136 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 2.1 14b Vace 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 2.1 14B Vace | Fast Image-to-Video API | WaveSpeedAI