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Wan 2.1 Synthetic to Real Ditto

wavespeed-ai /

WAN 2.1 Synthetic To Real Ditto mirrors motion and facial expressions in video-to-video synthetic-to-real conversion. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

video-to-video
इनपुट

निष्क्रिय

$0.2प्रति रन·~50 / $10

आगे:

उदाहरणसभी देखें

live-action film style version, realistic texture, cinematic lighting,Remove shadow

संबंधित मॉडल

README

WAN 2.1 Synthetic-To-Real Ditto — Video-to-Video Model

WAN 2.1 Synthetic-To-Real Ditto converts stylized or synthetic videos (3D, anime, game footage, VTuber-style avatars, etc.) into realistic, live-action–like footage. It mirrors the original motion and facial expressions while replacing the look with a more natural, photographic style. The model is exposed through a ready-to-use REST inference API with fast warm-starts and affordable pricing.

What it does

  • Takes a source video with a synthetic or stylized character.
  • Tracks body motion, facial expressions, and timing.
  • Generates a new realistic human version of the same performance.
  • Preserves framing and pacing so you can swap footage without re-editing your timeline.

This makes it ideal for:

  • Upgrading animated storyboards into realistic previews
  • Turning VTuber or game-style performances into semi-realistic actors
  • Rapid prototyping of live-action shots from synthetic previz

Key Capabilities

  • High-fidelity motion mirroring Copies head turns, eye blinks, lip movements, and body motion from the input clip with tight temporal alignment.
  • Synthetic-to-real translation Transforms toon, 3D, or heavily stylized characters into natural-looking humans while keeping their core identity and staging.
  • Consistent lighting and shading Adapts the original scene’s lighting so the new actor feels anchored in the same environment.
  • Resolution flexibility Supports both 480p and 720p output for different production needs.

Inputs and Controls

  • video (required) Upload or paste the URL of the source video.

  • resolution

  • 480p

  • 720p

Pricing

ResolutionPrice per secondMin billed secondsMin total priceMax billed secondsMax total price
480p$0.045 s$0.20120 s$4.80
720p$0.085 s$0.40120 s$9.60

How to Use

  1. Upload your synthetic or stylized video in the video field. (up to 120s)
  2. Select the desired resolution (480p or 720p).
  3. Make sure Enable Safety Checker is checked.
  4. Click Run.
  5. After processing, preview the real-style output in the right panel and download it for editing or further post-production.

Tips for Best Results

  • Use clips with clear, front-facing characters and stable framing to get the best facial detail.
  • Avoid heavy motion blur or rapid strobing; clean animation yields more faithful translations.
  • Keep clips short when iterating (around 3–5 seconds) to explore different looks quickly and control costs.
  • Once you find a look you like, batch-convert key shots from your project to build a consistent, realistic version of your synthetic footage.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

Wan 2.1 Synthetic To Real Ditto API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/synthetic-to-real-ditto 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 Synthetic To Real Ditto below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "resolution": "480p",
    "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/synthetic-to-real-ditto" \
  -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/synthetic-to-real-ditto";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "resolution": "480p",
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "resolution": "480p",
    "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/synthetic-to-real-ditto", 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 Synthetic To Real Ditto API — Frequently asked questions

What is the Wan 2.1 Synthetic To Real Ditto API?

Wan 2.1 Synthetic To Real Ditto is a WaveSpeedAI model for video editing, exposed as a REST API on WaveSpeedAI. WAN 2.1 Synthetic To Real Ditto mirrors motion and facial expressions in video-to-video synthetic-to-real conversion. 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 Synthetic To Real Ditto 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-synthetic-to-real-ditto.

How much does Wan 2.1 Synthetic To Real Ditto cost per run?

Wan 2.1 Synthetic To Real Ditto starts at $0.20 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 Synthetic To Real Ditto accept?

Key inputs: `video`, `resolution`, `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-synthetic-to-real-ditto.

How long does Wan 2.1 Synthetic To Real Ditto take to generate?

Median end-to-end generation time on WaveSpeedAI is around 163 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 Synthetic To Real Ditto 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 Synthetic to Real Ditto | AI Video Style Transfer API | WaveSpeedAI