Pruna P-Video Upscale enhances video resolution up to 4K while preserving the source aspect ratio and audio, with adjustable frame rate and an optional faster processing mode. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.01per run·~100 / $1
P-Video Upscale increases the resolution of an existing video while preserving its aspect ratio and original soundtrack. Choose output resolutions from 720p to 4K, adjust the frame rate, and optionally enable Turbo for faster processing.
720p, 1080p, 2K, or 4K.24, 30, or 60 FPS.| Parameter | Required | Description |
|---|---|---|
| video | Yes | Source video URL or upload. Maximum file size: 512 MiB. |
| target_resolution | No | Target resolution: 720p, 1080p, 2k, or 4k. Default: 1080p. |
| fps | No | Output frame rate: 24, 30, or 60. Default: 24. |
| turbo | No | Enable faster processing with a small quality trade-off. Default: false. |
720p, 1080p, 2k, or 4k.24, 30, or 60 FPS.Pricing is based on the source video duration, capped at the processing limit for the selected target_resolution and fps, then rounded up to the next whole second.
| Target Resolution | 24 / 30 FPS | 60 FPS |
|---|---|---|
| 720p | $0.01 / second | $0.02 / second |
| 1080p | $0.02 / second | $0.04 / second |
| 2k | $0.04 / second | $0.08 / second |
| 4k | $0.08 / second | $0.16 / second |
60 FPS uses twice the per-second rate of 24 or 30 FPS. Turbo does not add a surcharge. Changing fps can also change the processing limit and the number of billed seconds.
720p or 1080p for lower-cost processing before moving to higher resolutions.Resolution targets are based on pixel area. Exact output dimensions vary with the source aspect ratio; 720p does not always mean a height of 720 pixels.
Videos already larger than the selected target are not downscaled.
The maximum input file size is 512 MiB.
Clip length is limited by processed frames, not a fixed duration:
| Target | Maximum Processed Frames |
|---|---|
| 720p | 2,400 |
| 1080p | 1,200 |
| 2k | 600 |
| 4k | 300 |
Overlong videos are shortened before upscaling; only the beginning is retained. The remaining portion is not processed or billed.
The retained length depends on target_resolution and fps, with a small allowance reserved to stay within the frame limit. Billed duration is rounded up to the next whole second.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-video/upscale with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Video Upscale below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"target_resolution": "1080p",
"fps": 24,
"turbo": false
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/upscale" \
-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/upscale";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"target_resolution": "1080p",
"fps": 24,
"turbo": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"target_resolution": "1080p",
"fps": 24,
"turbo": False
}
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/upscale", 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)P Video Upscale is a Pruna Ai model for video upscaling, exposed as a REST API on WaveSpeedAI. Pruna P-Video Upscale enhances video resolution up to 4K while preserving the source aspect ratio and audio, with adjustable frame rate and an optional faster processing mode. Ready-to-use REST inference API, best performance, no coldstarts, 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/pruna-ai/pruna-ai-p-video-upscale.
P Video Upscale starts at $0.01 per run or per second. 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: `video`, `fps`, `target_resolution`, `turbo`. 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-upscale.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (Pruna Ai). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.