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.
Idle
$0.1per run·~10 / $1
Topaz Video Upscale Generative restores and upscales real and AI-generated video with Topaz Starlight diffusion models, powered by Topaz Labs models.
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.
| Parameter | Required | Description |
|---|---|---|
| video | Yes | The video to process. |
| model | No | The Topaz model to use. Default: Starlight Precise 2.6. |
| target_resolution | No | Target resolution to upscale to. The source aspect ratio is preserved. Options: 720p, 1080p, 2k, 4k. Default: 1080p. |
| Model | Best for |
|---|---|
| Starlight Precise 2.6 | High-quality generative upscaling and enhancement up to 4K (default; up to 9000 frames). |
| Starlight Precise 2.5 | Improved realism for AI-generated video (up to 9000 frames). |
| Starlight Precise 2 | Natural realism for GenAI video (up to 450 frames). |
| Starlight Precise 1 | First-generation precise diffusion upscaling (up to 9000 frames). |
| Starlight Fast 3 | The fastest Starlight model (up to 9000 frames). |
| Starlight Fast 2 | Fast diffusion upscaling with higher fidelity. |
| Starlight Fast 1 | First-generation fast generative upscaling (up to 9000 frames). |
| Starlight HQ | Maximum-quality generative enhancement for modern high-resolution footage. |
| Starlight Mini | Diffusion restoration for archival and degraded footage. |
| Starlight Sharp | Fast restoration with sharper detail. |
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 $0.10.
Starlight Precise 2.6 and switch if your content needs it.720p, 1080p, 2k, 4k.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/topaz/video/upscale-generative with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Video Upscale Generative below.
set -euo pipefail
: "${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
doneconst 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));
}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 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.
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.
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.
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.
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 (Topaz). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.