Pruna AI P Video Upscale API Documentation

Pruna AI P Video Upscale API Documentation

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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.

Features

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.


Why Choose This?

  • Up to 4K output — Upscale existing videos to 720p, 1080p, 2K, or 4K.
  • Original framing — Preserve the source video’s aspect ratio without stretching the image.
  • Audio preserved — Keep the original soundtrack when the source contains audio.
  • Frame-rate control — Choose an output frame rate of 24, 30, or 60 FPS.
  • Turbo processing — Enable faster processing with a small quality trade-off.

Parameters

ParameterRequiredDescription
videoYesSource video URL or upload. Maximum file size: 512 MiB.
target_resolutionNoTarget resolution: 720p, 1080p, 2k, or 4k. Default: 1080p.
fpsNoOutput frame rate: 24, 30, or 60. Default: 24.
turboNoEnable faster processing with a small quality trade-off. Default: false.

How to Use

  1. Upload a video — Provide the source video or its URL.
  2. Choose a resolution — Select 720p, 1080p, 2k, or 4k.
  3. Set frame rate optional — Choose an output frame rate of 24, 30, or 60 FPS.
  4. Enable Turbo optional — Turn on Turbo when faster processing is preferred.
  5. Submit — Process and retrieve the upscaled MP4.

Pricing

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 Resolution24 / 30 FPS60 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.


Best Use Cases

  • Social videos — Prepare existing clips for higher-resolution publishing.
  • Presentation footage — Improve video quality for larger screens and displays.
  • Video libraries — Upgrade existing footage while preserving its framing and soundtrack.
  • Marketing content — Improve existing promotional and product videos.
  • Archived footage — Create higher-resolution versions of older video assets.

Pro Tips

  • Use 720p or 1080p for lower-cost processing before moving to higher resolutions.
  • Choose the frame rate based on the motion and source footage.
  • Enable Turbo when processing speed is more important than maximum quality.
  • Keep the source aspect ratio suitable for the intended publishing platform.
  • Higher resolutions support fewer processed frames per request.

Notes

  • 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:

    TargetMaximum Processed Frames
    720p2,400
    1080p1,200
    2k600
    4k300
  • 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.


Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-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 once. Never retry this request automatically:
# a retry after an unclear failure can create a second, billed task.
CHECK='The task may already exist. Check History or the predictions list before submitting again.'
if ! SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  --connect-timeout 10 --max-time 60 \
  -X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/upscale" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}"); then
  printf 'Submit failed. %s\n' "${CHECK}" >&2
  exit 1
fi

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id. %s\n' "${CHECK}" >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

# 2. Poll until the prediction reaches a final status.
BODY_FILE=$(mktemp)
trap 'rm -f "${BODY_FILE}"' EXIT
while true; do
  # Transient errors (429, 5xx) are retried, honoring Retry-After.
  HTTP_CODE=$(curl --silent --show-error --fail-with-body \
    --connect-timeout 10 --max-time 60 --retry 5 --retry-delay 2 \
    --output "${BODY_FILE}" --write-out '%{http_code}' \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}" || true)
  case "${HTTP_CODE}" in
    2??) ;;
    000|429|5??)  # dropped connection or temporary error: keep polling
      printf 'Task %s: HTTP %s, polling again in 10s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      sleep 10; continue ;;
    *) printf 'Task %s: HTTP %s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      cat "${BODY_FILE}" >&2; exit 1 ;;
  esac
  RESULT=$(jq 'if type == "object" and has("data") then .data else . end' "${BODY_FILE}")
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted)
      printf 'Task %s ended with status %s\n' "${PREDICTION_ID}" "${STATUS}" >&2
      printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done

Retry temporary errors only while polling, never the submit request. Errors, retries and timeouts

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
videostringYes1 ~ unlimited charactersSource video to upscale, up to 512 MiB. Overlong videos are trimmed from the start based on the target resolution and frame rate.
target_resolutionstringNo1080p720p, 1080p, 2k, 4kTarget resolution. The source aspect ratio is preserved.
fpsintegerNo2424, 30, 60Output frame rate. Choose 24, 30, or 60 FPS.
turbobooleanNofalse-Process faster with a small quality trade-off.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.statusstringTask status. completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses.
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction
data.modelstringModel ID used for the prediction
data.outputsarray<string | object>Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.
data.urlsobjectObject containing related API endpoints
data.statusstringStatus: completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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