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Wan 2.1 Mocha

wavespeed-ai /

MoCha performs Video-To-Video character swaps using reference images, replacing a video's character without per-frame pose or depth maps. Ready-to-use REST inference API, no coldstarts, affordable pricing.

video-to-video
Đầu vào

Chờ

$0.2cho mỗi lần chạy·~50 / $10

Tiếp theo:

Ví dụXem tất cả

Replace the man in the video with the image. Just change the face. Make the details normally

Let the woman in the image sing the song naturally.

Replace the man in the video with the image I give you. Not only the face, but also the suit.

Replace the man in the video with the image I give you.

Change the man in the video to the image man. change the face and haircut

Mô hình liên quan

README

MoCha 🎭 — AI Video Character Replacement

MoCha is an end-to-end video character replacement system that seamlessly swaps the main character in a video with a new one provided via reference images. Unlike traditional methods, it requires no explicit per-frame structural guidance (such as pose or depth maps), while maintaining realistic motion, lighting, and facial expressions throughout the clip.

🌟 Key Features

  • 🧠 Structure-Free Replacement No need for pose or depth maps — MoCha automatically aligns motion, expression, and body posture.

  • 🎥 Motion Preservation Accurately transfers the source actor’s motion, emotion, and camera perspective to the target character.

  • 🎨 Identity Consistency Maintains the new character’s facial identity, lighting, and style across frames without flickering.

  • ⚙️ Easy Setup Works with a single image and a source video — no need for complex preprocessing or rigging.

  • 💡 High Realism, Low Effort Perfect for film, advertising, digital avatars, and creative character transformation.

💰 Pricing

ResolutionPrice per 5sPrice per secondMax Length
480p$0.20$0.04 / s120 s
720p$0.40$0.08 / s120 s

Billing Rules

  • Minimum charge: 5 seconds - any video shorter than 5 seconds is billed as 5 seconds.
  • Maximum billed duration: 120 seconds (2 minutes)

⚙️ How to Use

  1. Upload image — A clear reference image of the new character (recommended formats: JPG / PNG, avoid WEBP).
  2. Upload video — The motion source; MoCha extracts pose and expression dynamics from this clip.
  3. Add prompt (optional) — Guide the output, e.g. “preserve outfit; natural expressions; no background changes.”
  4. Select resolution — Choose between 480p or 720p.
  5. Generate — Wait a moment while MoCha processes the replacement.
  6. Review & Iterate — Fix a seed to reproduce results, or vary it for A/B comparisons.

🧩 Tips for Best Results

  • Match Pose & Composition: Keep your reference image’s camera angle, body orientation, and framing close to the target video.
  • Keep Aspect Ratios Consistent: Use the same aspect ratio between your input image and video.
  • Limit Video Length: For best stability, keep clips under 60 seconds — longer clips may show slight quality degradation.
  • Lighting Consistency: Match lighting direction and tone between image and video to minimize blending artifacts.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Wan 2.1 Mocha API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/mocha 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 Mocha below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "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/mocha" \
  -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/mocha";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "resolution": "480p",
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "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/mocha", 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 Mocha API — Frequently asked questions

What is the Wan 2.1 Mocha API?

Wan 2.1 Mocha is a WaveSpeedAI model for video editing, exposed as a REST API on WaveSpeedAI. MoCha performs Video-To-Video character swaps using reference images, replacing a video's character without per-frame pose or depth maps. Ready-to-use REST inference API, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Wan 2.1 Mocha 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-mocha.

How much does Wan 2.1 Mocha cost per run?

Wan 2.1 Mocha 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 Mocha accept?

Key inputs: `prompt`, `image`, `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-mocha.

How long does Wan 2.1 Mocha take to generate?

Median end-to-end generation time on WaveSpeedAI is around 392 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 Mocha 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 Mocha | AI Video Character Swap API | WaveSpeedAI