Topaz Video Upscale upscales and enhances video up to 4K with Topaz's precision Proteus, Artemis, Gaia, Theia, Iris and Dione models. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.1per run·~10 / $1
Topaz Video Upscale upscales and enhances video up to 4K with Topaz's precision Proteus, Artemis, Gaia, Theia, Iris and Dione models, powered by Topaz Labs models.
Professional Topaz quality
The same models used by photographers, filmmakers and studios.
Choose the right model
21 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: Proteus. |
| target_resolution | No | Target resolution to upscale to. The source aspect ratio is preserved. Options: 720p, 1080p, 2k, 4k. Default: 1080p. |
| Model | Best for |
|---|---|
| Proteus | The default choice for video enhancement and upscaling (default). |
| Proteus Natural | Next-generation natural enhancement; always upscales 2x. |
| Rhea | High-fidelity upscaling for fine detail. |
| Artemis High Quality | Enhancement for high-quality sources. |
| Artemis Medium Quality | Balanced enhancement for medium-quality footage. |
| Artemis Low Quality | Heavier restoration for low-quality footage. |
| Artemis Medium Halo | Removes moderate edge haloing. |
| Artemis Strong Halo | Removes pronounced edge haloing. |
| Artemis Aliasing & Moire | Reduces aliasing and moire. |
| Gaia High Quality | Fine detail for already-clean sources. |
| Gaia CG | Upscaling for animation, CG and stylized footage. |
| Gaia 2 Animation | Animation upscaling; always upscales 2x. |
| Theia Fine Tune Detail | Aggressive per-frame detail enhancement. |
| Theia Fine Tune Fidelity | Stable, consistent detail with high fidelity. |
| Iris Medium Quality | Face recovery for mid-quality footage. |
| Iris Low Quality | Face recovery for degraded footage. |
| Dione DV | Deinterlacing for DV sources. |
| Dione TV | Deinterlacing for TV sources. |
| Dione Robust | Deinterlacing for challenging interlaced content. |
| Dione Dehalo | Deinterlacing plus halo removal. |
| Dione Robust Dehalo | Robust deinterlacing plus halo removal. |
$0.20 per gigapixel processed, where gigapixels = (width × step) × (height × step) × frames ÷ 1024³ and the step is 1 for a scale up to 1.2x, 2 up to 2.4x and 4 above. Gaia 2 Animation: $0.10 per 300 frames up to 1080p output, per 100 frames above. Minimum $0.10.
Proteus and switch if your content needs it.720p, 1080p, 2k, 4k.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/topaz/video/upscale with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Video Upscale below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"model": "Proteus",
"target_resolution": "1080p"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/topaz/video/upscale" \
-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";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"model": "Proteus",
"target_resolution": "1080p"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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": "Proteus",
"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", 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 is a Topaz model for video upscaling, exposed as a REST API on WaveSpeedAI. Topaz Video Upscale upscales and enhances video up to 4K with Topaz's precision Proteus, Artemis, Gaia, Theia, Iris and Dione 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.
Video Upscale 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.
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.