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Wan 2.1 V2V 480P LoRA Ultra Fast

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Wan 2.1 V2V 480p is an ultra-fast video-to-video model that generates unlimited AI videos and supports custom LoRAs for personalization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

lora-support
Entrée

En attente

$0.125par exécution·~80 / $10

ExemplesTout voir

A pixel-art animation of a red-suited chibi character jumping between floating platforms in a bright blue sky. The motion is bouncy and playful, with pixel particle effects on each jump. The camera follows smoothly with slight shake to enhance the retro arcade game feel.

An anime-style video of a pink-haired high school girl walking slowly under cherry blossom trees. The petals fall gently around her as the camera follows her from the side with a soft dolly shot. Her expression is peaceful, her skirt and hair moving naturally in the spring breeze. The background is softly blurred, with warm pastel lighting.

The woman in the image slowly turns toward the camera and smiles, hair gently moving in the breeze, sunlight flickering"

A fantasy-themed video of a cloaked traveler seen from behind, standing motionless on a cliff edge. The camera slowly pulls back and rises to reveal the floating island in the sky, surrounded by drifting clouds and ethereal light. The mood is mysterious and epic, with orchestral music vibes.

The character in the image starts singing emotionally on a small spotlighted stage, camera slowly zooming in on her face

The subject walks slowly through a crowded market street at night, lens flare from passing headlights, immersive ambiance

The camera rotates around the frozen figure as their clothing slowly melts into animated paint strokes

A slow motion shot of a woman walking through a street market at sunset, soft golden light reflecting off fresh produce and colorful textiles, vendors chatting and people smiling in the background. The camera gently tracks her from behind as her hair sways.

The character dissolves into flower petals that spiral upward into the sky, surreal and magical ambiance

The person in the image begins to dissolve into liquid chrome, reflections warping around a moving camera

Modèles associés

README

Wan 2.1 V2V 480p LoRA Ultra Fast — wavespeed-ai/wan-2.1/v2v-480p-lora-ultra-fast

Wan 2.1 V2V 480p LoRA Ultra Fast is a speed-optimized video-to-video model for prompt-guided edits while preserving the original motion and timing of an input video. Upload a source video, describe what should change, and tune strength to control how closely the output follows the original footage. It supports up to 3 LoRAs to enforce a consistent style, character look, or branded aesthetic—now with lower latency for rapid iteration.

Key capabilities

  • Ultra-fast video-to-video transformation anchored to an input video (480p output)
  • Prompt-guided edits while keeping motion continuity and pacing
  • Strength control to balance preservation vs. transformation
  • LoRA support (up to 3) for stable style/identity steering
  • Fine control over motion behavior via flow_shift

Use cases

  • Rapid V2V restyling for social clips and creative iteration
  • Apply a consistent “house style” across multiple clips using LoRAs
  • Lighting/mood changes (cinematic grade, neon, golden hour) without re-animating motion
  • Brand-safe refresh: keep composition and timing, update textures/colors/details
  • Quick A/B testing by changing prompts, LoRAs, or seed

Pricing

DurationPrice per video
5s$0.125
10s$0.1875

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)
  • loras (optional): up to 3 LoRA items for style/identity steering

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)

LoRA (up to 3 items):

  • loras: list of LoRA entries (max 3)

  • path: owner/model-name or a direct.safetensors URL

  • scale: LoRA strength

Prompting guide (V2V)

Write prompts that explicitly separate preservation from transformation:

Template: Keep the same camera motion and timing from the input video. Change [style/lighting/environment]. Keep faces natural and consistent. Avoid flicker and warping.

Example prompts

  • Keep the original motion and composition. Apply a candid, cinematic look with warm sunlight, soft depth of field, and natural skin texture.
  • Preserve timing and camera movement. Restyle into a clean anime look with consistent shading and no flicker.
  • Keep the same scene and people. Change the color grade to sunset golden hour, add subtle lens flare, maintain realistic shadows.
Remarque :Ce site utilise des modèles d'IA fournis par des tiers.

Wan 2.1 v2v 480p Lora 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-480p-lora-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 480p Lora 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-480p-lora-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-480p-lora-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-480p-lora-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 480p Lora Ultra Fast API — Frequently asked questions

What is the Wan 2.1 v2v 480p Lora Ultra Fast API?

Wan 2.1 v2v 480p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Wan 2.1 V2V 480p is an ultra-fast video-to-video model that generates unlimited AI videos and supports custom LoRAs for personalization. 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 480p Lora 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-480p-lora-ultra-fast.

How much does Wan 2.1 v2v 480p Lora Ultra Fast cost per run?

Wan 2.1 v2v 480p Lora Ultra Fast 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.

What inputs does Wan 2.1 v2v 480p Lora 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-480p-lora-ultra-fast.

How do I get started with the Wan 2.1 v2v 480p Lora Ultra Fast 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 2.1 v2v 480p Lora 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 480P LoRA Ultra Fast | Custom LoRA Image API | WaveSpeedAI