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Topaz Video Upscale Generative API

topaz/video/upscale-generative

Topaz Video Upscale Generative restores and upscales real and AI-generated video with Topaz Starlight diffusion models. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Upscale Video
Input

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Idle

topaz/video/upscale-generative preview unavailable

$0.1per run·~10 / $1

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README

Topaz Video Upscale Generative

Topaz Video Upscale Generative restores and upscales real and AI-generated video with Topaz Starlight diffusion models, powered by Topaz Labs models.

Why Choose This?

  • Professional Topaz quality
    The same models used by photographers, filmmakers and studios.

  • Choose the right model
    10 Topaz models, each tuned for a different kind of video.

  • Pay for what you process
    Pricing follows the size of the output, matching Topaz's own rates.

Parameters

ParameterRequiredDescription
videoYesThe video to process.
modelNoThe Topaz model to use. Default: Starlight Precise 2.6.
target_resolutionNoTarget resolution to upscale to. The source aspect ratio is preserved. Options: 720p, 1080p, 2k, 4k. Default: 1080p.

Models

ModelBest for
Starlight Precise 2.6High-quality generative upscaling and enhancement up to 4K (default; up to 9000 frames).
Starlight Precise 2.5Improved realism for AI-generated video (up to 9000 frames).
Starlight Precise 2Natural realism for GenAI video (up to 450 frames).
Starlight Precise 1First-generation precise diffusion upscaling (up to 9000 frames).
Starlight Fast 3The fastest Starlight model (up to 9000 frames).
Starlight Fast 2Fast diffusion upscaling with higher fidelity.
Starlight Fast 1First-generation fast generative upscaling (up to 9000 frames).
Starlight HQMaximum-quality generative enhancement for modern high-resolution footage.
Starlight MiniDiffusion restoration for archival and degraded footage.
Starlight SharpFast restoration with sharper detail.

Pricing

Priced per output frame. Up to 1080p output: $0.10 per 26.04 frames (Starlight Precise 1: per 10 frames; Starlight Fast 1 and 2: per 52.08 frames). Above 1080p: $0.10 per 11.92 frames (Starlight Precise 1: per 6.02 frames; Starlight Fast 1 and 2: per 23.84 frames). Minimum $0.10.

How to Use

  1. Upload your video — Provide a URL or upload a file.
  2. Pick a model — Start with the default Starlight Precise 2.6 and switch if your content needs it.
  3. Choose the target resolution — 720p, 1080p, 2k, 4k.
  4. Submit — Download the result when it is ready.
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.

Video Upscale Generative API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/topaz/video/upscale-generative 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 Video Upscale Generative below.

HTTP example
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",
    "model": "Starlight Precise 2.6",
    "target_resolution": "1080p"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/topaz/video/upscale-generative" \
  -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/topaz/video/upscale-generative";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "model": "Starlight Precise 2.6",
        "target_resolution": "1080p"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "model": "Starlight Precise 2.6",
    "target_resolution": "1080p"
}

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/topaz/video/upscale-generative", 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)

Video Upscale Generative API — Frequently asked questions

What is the Video Upscale Generative API?

Video Upscale Generative is a Topaz model for video upscaling, exposed as a REST API on WaveSpeedAI. Topaz Video Upscale Generative restores and upscales real and AI-generated video with Topaz Starlight diffusion models. 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 Video Upscale Generative 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/topaz/topaz-video-upscale-generative.

How much does Video Upscale Generative cost per run or per second?

Video Upscale Generative starts at $0.1 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.

What inputs does Video Upscale Generative accept?

Key inputs: `video`, `model`, `target_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/topaz/topaz-video-upscale-generative.

How do I get started with the Video Upscale Generative API?

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.

Can I use Video Upscale Generative outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Topaz). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

llms.txt — topaz/video/upscale-generative for AI agents and LLMs