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Wan 2.1 V2V 720P Ultra Fast

wavespeed-ai /

Ultra-fast Wan 2.1 V2V generates unlimited 720P video-to-video conversions and supports custom LoRAs for personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

video-to-video
Input

Siap

$0.225per run·~44 / $10

Selanjutnya:

ContohLihat semua

A low-poly style girl explorer standing on a polygonal mountain cliff, stylized sharp edges and flat color shading, minimal facial details but expressive posture, wind gently affecting her polygonal cape and hair, pastel-colored low-poly clouds drifting behind, slow camera fly-around motion, stylized geometric aesthetic, clean cinematic video

Masterpiece, ultra-detailed, photorealistic portrait of an ancient wise wizard with glowing eyes, dramatic volumetric lighting, cinematic, studio shot, sharp focus, 8k, award-winning photography.

A majestic golden retriever, with sun-drenched fur and playful eyes, running through a field of vibrant green grass under a clear blue sky, golden hour lighting, cinematic photography.

An inquisitive squirrel, busily burying nuts in a sun-dappled forest floor. Leaves crunch softly under its tiny paws, and dappled light filters through the canopy. Close-up, natural lighting, peaceful and lively ambiance.

A young elephant balances on a large rubber ball in a dusty circus ring, flapping its ears for balance as children cheer.

A lion tamer enters the ring under a golden spotlight, facing a majestic lion. The camera captures the tension from behind the cage bars, cutting to the lion's eyes and the flick of the whip. Dust rises with every step. High cinematic drama.

A group of flamingos strut through shallow water under a bright blue sky, their pink feathers catching shimmering reflections.

A skier glides downhill, carving turns that kick up clouds of snow. The camera follows steadily as he picks up speed along the slope.

A man in a reflective silver spacesuit walks alone through a dusty Martian outpost, solar panels creaking in the wind, with a distant sandstorm slowly approaching.

A bright orange sports car speeds along a wet racetrack. [Low-angle shot] captures the stormy sky as lightning crackles above. The camera gradually lifts, revealing the full rainy-night race environment glowing under powerful lights.

Model Terkait

README

Wan 2.1 V2V 720p Ultra Fast — wavespeed-ai/wan-2.1/v2v-720p-ultra-fast

Wan 2.1 V2V 720p Ultra Fast is a speed-optimized video-to-video model that transforms an input video using a text prompt while preserving the original motion and timing. Upload a source video, describe the desired changes (style, lighting, environment, details), and tune strength to control how closely the output follows the original footage. This variant is the non-LoRA version, built for fast, clean V2V iteration at 720p.

Key capabilities

  • Ultra-fast video-to-video transformation anchored to an input video (720p output)
  • Prompt-guided edits while keeping motion continuity and pacing
  • Strength control to balance preservation vs. transformation
  • Fine motion behavior tuning via flow_shift for smoother motion
  • Efficient for rapid A/B testing with different prompts and seeds

Use cases

  • Rapid 720p V2V restyling for social, ads, and creative iteration
  • Mood and lighting changes (cinematic grade, warm window light, neon, noir)
  • Brand-safe refresh: keep composition and timing, update textures/colors/details
  • Consistent motion preservation when you only need prompt-driven changes
  • Fast iteration before upgrading to higher resolution or LoRA workflows

Pricing

DurationPrice per video
5s$0.225
10s$0.3375

Inputs

  • video (required): source video to transform
  • prompt (required): what to change and how the result should look
  • negative_prompt (optional): what to avoid (artifacts, jitter, unwanted elements)

Parameters

  • num_inference_steps: sampling steps
  • duration: output duration (seconds)
  • strength: how strongly to transform the input video (lower = preserve more; higher = change more)
  • guidance_scale: prompt adherence strength
  • flow_shift: motion/flow behavior tuning
  • seed: random seed (-1 for random; fixed for reproducible results)

Prompting guide (V2V)

A clean structure is “preserve + edit + constraints”:

Template: Keep the original motion and timing. Change [style/lighting/environment/details]. Keep faces stable and natural. Avoid flicker, warping, and jitter.

Example prompts

  • Keep the original motion and composition. Apply a candid, cinematic look with warm sunlight, soft depth of field, and gentle film grain.
  • Preserve timing and camera movement. Restyle the scene into a clean anime look with stable shading and no flicker.
  • Keep the same scene and people. Shift the color grade to golden hour and add subtle bloom while maintaining realistic shadows.
Catatan:Situs web ini menggunakan model AI yang disediakan oleh pihak ketiga.

Wan 2.1 v2v 720p Ultra Fast API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/v2v-720p-ultra-fast 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 v2v 720p Ultra Fast 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",
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "num_inference_steps": 30,
    "duration": 5,
    "strength": 0.9,
    "guidance_scale": 5,
    "flow_shift": 3,
    "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/v2v-720p-ultra-fast" \
  -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/v2v-720p-ultra-fast";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "num_inference_steps": 30,
        "duration": 5,
        "strength": 0.9,
        "guidance_scale": 5,
        "flow_shift": 3,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "num_inference_steps": 30,
    "duration": 5,
    "strength": 0.9,
    "guidance_scale": 5,
    "flow_shift": 3,
    "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/v2v-720p-ultra-fast", 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 v2v 720p Ultra Fast API — Frequently asked questions

What is the Wan 2.1 v2v 720p Ultra Fast API?

Wan 2.1 v2v 720p Ultra Fast is a WaveSpeedAI model for video editing, exposed as a REST API on WaveSpeedAI. Ultra-fast Wan 2.1 V2V generates unlimited 720P video-to-video conversions and supports custom LoRAs for personalized styles. 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 v2v 720p Ultra Fast 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-v2v-720p-ultra-fast.

How much does Wan 2.1 v2v 720p Ultra Fast cost per run?

Wan 2.1 v2v 720p Ultra Fast starts at $0.23 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 v2v 720p Ultra Fast accept?

Key inputs: `prompt`, `video`, `duration`, `seed`, `guidance_scale`, `num_inference_steps`. 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-v2v-720p-ultra-fast.

How long does Wan 2.1 v2v 720p Ultra Fast take to generate?

Median end-to-end generation time on WaveSpeedAI is around 182 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 v2v 720p Ultra Fast 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 V2V 720P Ultra Fast | AI Video to Video API | WaveSpeedAI