Pruna AI P-Video Avatar is a fast AI avatar video generation model that creates high-quality avatar videos for digital humans, talking characters, social media content, marketing creatives, virtual presenters, and AI video workflows. Ready-to-use REST inference API with simple integration, no coldstarts, and affordable pricing.
Chờ
$0.025cho mỗi lần chạy·~40 / $1
Pruna AI P-Video Avatar generates a talking or performing avatar video from a reference image and an audio clip, with optional prompt guidance for motion and expression. It is designed for character-driven video generation where the image defines the avatar and the audio drives the timing and delivery.
Image + audio avatar generation Combine a reference image with an audio track to generate a video avatar performance.
Prompt-guided motion control
Use video_prompt to nudge expression, movement, or overall performance style.
Simple output settings Choose resolution and seed without a heavy configuration workflow.
Audio-driven timing Video length follows the uploaded audio duration, making it easier to generate synced outputs.
Production-ready workflow Useful for avatar clips, talking portraits, character presentations, and short-form content generation.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Reference image used as the avatar source. |
| audio | Yes | Audio file used to drive the avatar video. |
| video_prompt | No | Optional prompt describing expression, motion, or overall video style. Keep it simple for better stability. |
| resolution | No | Output resolution: 720p or 1080p. |
| seed | No | Random seed for reproducibility. Use the same seed for more consistent results. |
720p for lower cost or 1080p for higher quality.Natural head movement, subtle facial expression, stable identity, clean speaking performance, realistic motion
Pricing is based on the audio duration and resolution.
| Audio Duration | Cost |
|---|---|
| 5s | $0.125 |
| 10s | $0.25 |
| 30s | $0.75 |
| 60s | $1.50 |
| 600s | $15.00 |
| Audio Duration | Cost |
|---|---|
| 5s | $0.225 |
| 10s | $0.45 |
| 30s | $1.35 |
| 60s | $2.70 |
| 600s | $27.00 |
audio duration720p uses a base rate of $0.025 per second1080p costs 1.8× the 720p ratevideo_prompt and seed do not affect pricingvideo_prompt simple and direct — overly detailed prompts are more likely to fail.720p for testing, then switch to 1080p for final-quality outputs.seed when you want more consistent variations.image and audio are required.video_prompt is simple rather than highly detailed.save_audio is not exposed in the current input settings shown here.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-video/avatar with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Video Avatar below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
"video_prompt": "The person is talking.",
"resolution": "720p",
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/avatar" \
-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/avatar";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
"video_prompt": "The person is talking.",
"resolution": "720p",
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"video_prompt": "The person is talking.",
"resolution": "720p",
"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/pruna-ai/p-video/avatar", 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)P Video Avatar is a Pruna Ai model for talking-avatar generation, exposed as a REST API on WaveSpeedAI. Pruna AI P-Video Avatar is a fast AI avatar video generation model that creates high-quality avatar videos for digital humans, talking characters, social media content, marketing creatives, virtual presenters, and AI video workflows. Ready-to-use REST inference API with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.
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/pruna-ai/pruna-ai-p-video-avatar.
P Video Avatar starts at $0.025 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.
Key inputs: `image`, `audio`, `resolution`, `seed`, `video_prompt`. 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/pruna-ai/pruna-ai-p-video-avatar.
Median end-to-end generation time on WaveSpeedAI is around 25 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Pruna Ai). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.