Pruna AI P-Video Text to Video is a fast AI video generation model that creates high-quality videos from text prompts. Ready-to-use REST inference API for cinematic clips, social media videos, advertising creatives, product visuals, motion design, and AI video generation workflows with simple integration, no coldstarts, and affordable pricing.
Chờ
$0.02cho mỗi lần chạy·~50 / $1
An adult man stands on a vast desert highway under warm sunlight, with distant mountains and dry air haze behind him. Dust moves subtly across the road, his clothes shift gently in the wind, and he looks toward the horizon before turning slightly toward the camera. The camera slowly pulls back to reveal the open landscape. Cinematic commercial travel film, realistic motion, stable identity, warm natural light.
Pruna AI P-Video Text-to-Video generates videos from natural-language prompts with control over aspect ratio, duration, resolution, audio output, and seed-based reproducibility. It is suitable for cinematic scenes, commercial visuals, social content, concept videos, and other prompt-driven video generation workflows.
Prompt-based video generation Turn natural-language scene descriptions into motion video clips with controllable style and composition.
Flexible format control Choose aspect ratio, duration, and resolution to match different delivery needs.
Optional audio saving
Enable save_audio when you want the generated output saved with audio.
Seed support for reproducibility
Use seed to get more consistent outputs across repeated generations.
Simple pricing model Cost scales clearly with duration and output resolution.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the desired video scene, motion, and style. |
| aspect_ratio | No | Output aspect ratio, such as 16:9. |
| duration | No | Video duration in seconds. |
| resolution | No | Output resolution: 720p or 1080p. |
| save_audio | No | Whether to save the generated video with audio. |
| seed | No | Random seed for reproducibility. Use the same seed to get more consistent results. |
720p for lower cost or 1080p for higher quality.save_audio if you want the saved result to include audio.An adult man stands on a vast desert highway under warm sunlight, with distant mountains and dry air haze behind him. Dust moves subtly across the road, his clothes shift gently in the wind, and he looks toward the horizon before turning slightly toward the camera. The camera slowly pulls back to reveal the open landscape. Cinematic commercial travel film, realistic motion, stable identity, warm natural light.
Pricing is based on duration and resolution.
| Resolution | Per Second |
|---|---|
| 720p | $0.02 |
| 1080p | $0.04 |
| Resolution | 5s | 10s | 15s |
|---|---|---|---|
| 720p | $0.10 | $0.20 | $0.30 |
| 1080p | $0.20 | $0.40 | $0.60 |
720p costs $0.02 per second1080p costs 2× the 720p ratedurationaspect_ratio, save_audio, and seed do not affect pricing720p for rapid testing, then switch to 1080p for higher-quality final outputs.seed when you want to iterate on a concept with more consistent results.aspect_ratio to the final platform, such as widescreen for cinematic layouts.prompt is the only required field.duration and resolution.save_audio controls whether the saved video includes audio, but does not affect pricing.seed helps with reproducibility but may not guarantee identical results in every case.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-video/text-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Video Text To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"save_audio": true,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/text-to-video" \
-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/text-to-video";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"save_audio": true,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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 = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"save_audio": True,
"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/text-to-video", 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 Text To Video is a Pruna Ai model for video generation, exposed as a REST API on WaveSpeedAI. Pruna AI P-Video Text to Video is a fast AI video generation model that creates high-quality videos from text prompts. Ready-to-use REST inference API for cinematic clips, social media videos, advertising creatives, product visuals, motion design, and AI video generation workflows 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-text-to-video.
P Video Text To Video starts at $0.020 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: `prompt`, `aspect_ratio`, `resolution`, `duration`, `seed`, `save_audio`. 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-text-to-video.
Median end-to-end generation time on WaveSpeedAI is around 11 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.