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VEED Fabric 1.0 turns one image into dynamic, talking videos and AI avatars in 480p or 720p (starts at $0.35/5s 480p, $0.7/5s 720p). Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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$0.35per run·~28 / $10

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VEED Fabric 1.0

An image-to-video API that brings a single still image to life as a dynamic, talking video. Ideal for video storytelling, personalized messages, digital avatars, and automated content pipelines.

Why it looks good

  • One image + Audio → talking video: Generate lip-synced, expressive clips from a single portrait or character image.
  • Natural lip-sync & expressions: Stable mouth–audio alignment and smooth facial transitions with minimal jitter.
  • Short creation pipeline: Image + voice → finished video, optimized for batch runs and automation.
  • Multi-scenario ready: Explainers, greetings, brand characters, course intros, and support avatars.

Pricing

Billing duration: Input media duration is rounded up to whole seconds. The minimum billed duration is 3 seconds; shorter inputs are billed as 3 seconds. Existing per-5-second rate tables remain unchanged.

ResolutionPrice per 5 secondsExample (10s)Example (15s)
480p$0.35$0.70$1.05
720p$0.70$1.40$2.10

Our endpoint starts at $0.35 per 5 seconds (480p) or $0.70 per 5 seconds (720p) for video generation.

How to use

  1. Upload audio
  • Add a voice track URL or drag-and-drop a file into the audio field.
  • Use clean, paced speech; denoise/EQ if possible.
  1. Upload image
  • Paste an image URL or drag-and-drop a portrait into the image field.
  • Prefer a clear front or body view with even lighting.
  1. Select resolution
  • Choose 480p for lightweight clips or 720p for sharper output.
  1. Run
  • Click Run to start the job. You’ll receive a job ID; the UI will show progress.
  1. Retrieve & iterate
  • Download the result when complete.
  • Swap audio or image, or adjust resolution to iterate quickly.

Common use cases

  • Digital avatars
  • Personalized greetings
  • Education snippets
  • Social/marketing upgrades (poster → talking video)
  • Customer service presenters
Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Fabric 1.0 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/veed/fabric-1.0 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 Fabric 1.0 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",
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "resolution": "480p"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/veed/fabric-1.0" \
  -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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/veed/fabric-1.0";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
        "resolution": "480p"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  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",
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "resolution": "480p"
}

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/veed/fabric-1.0", 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 = 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", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

Fabric 1.0 API — Frequently asked questions

What is the Fabric 1.0 API?

Fabric 1.0 is a Veed model for talking-avatar generation, exposed as a REST API on WaveSpeedAI. VEED Fabric 1.0 turns one image into dynamic, talking videos and AI avatars in 480p or 720p (starts at $0.35/5s 480p, $0.7/5s 720p). 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 Fabric 1.0 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/veed/veed-fabric-1.0.

How much does Fabric 1.0 cost per run?

Fabric 1.0 starts at $0.35 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 Fabric 1.0 accept?

Key inputs: `image`, `audio`, `resolution`. 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/veed/veed-fabric-1.0.

How long does Fabric 1.0 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 571 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 Fabric 1.0 outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Veed). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Fabric 1.0 | AI Digital Human API on WaveSpeedAI