Topaz Image Upscale API

topaz/image/upscale

Topaz Image Upscale enlarges photos to 2K, 4K or 8K with precise, non-generative Topaz upscaling models. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Upscale Image
Input

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Idle

topaz/image/upscale preview unavailable

$0.1per run·~10 / $1

Related Models

README

Topaz Image Upscale

Topaz Image Upscale enlarges photos to 2K, 4K or 8K with precise, non-generative Topaz upscaling 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
    6 Topaz models, each tuned for a different kind of image.

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

Parameters

ParameterRequiredDescription
imageYesThe image to process (JPEG, PNG, TIFF or WebP).
modelNoThe Topaz model to use. Default: Standard V2.
target_resolutionNoThe target resolution of the output: 2k (about 4 MP), 4k (about 16.8 MP) or 8k (about 67 MP). The source aspect ratio is preserved. Options: 2k, 4k, 8k. Default: 4k.
output_formatNoThe format of the output image. Options: jpeg, png. Default: jpeg.

Models

ModelBest for
Standard V2General-purpose upscaling balancing detail, sharpness and noise (default).
High Fidelity V2Faithful upscaling for high-quality sources.
Upscale High Fidelity V3Improved high-fidelity upscaling for high-resolution images.
Low Resolution V2Recovery-focused upscaling for small, low-resolution images.
CGIPrecise upscaling for artwork, illustrations and CG renders.
Text RefineClean upscaling for text, graphics and shapes.

Pricing

2k: $0.10 per image. 4k: $0.10 per image. 8k: $0.30 per image.

How to Use

  1. Upload your image — Provide a URL or upload a file.
  2. Pick a model — Start with the default Standard V2 and switch if your content needs it.
  3. Choose the target resolution — 2k, 4k, 8k.
  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 Documentation prices are for reference and may be outdated.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.

Image Upscale API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/topaz/image/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 Image Upscale below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "model": "Standard V2",
    "target_resolution": "4k",
    "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/topaz/image/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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/topaz/image/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "model": "Standard V2",
        "target_resolution": "4k",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "model": "Standard V2",
    "target_resolution": "4k",
    "output_format": "jpeg"
}

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/image/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)

Image Upscale API — Frequently asked questions

What is the Image Upscale API?

Image Upscale is a Topaz model for image upscaling, exposed as a REST API on WaveSpeedAI. Topaz Image Upscale enlarges photos to 2K, 4K or 8K with precise, non-generative Topaz upscaling 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 Image Upscale 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-image-upscale.

How much does Image Upscale cost per run or per second?

Image 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.

What inputs does Image Upscale accept?

Key inputs: `image`, `model`, `output_format`, `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-image-upscale.

How do I get started with the Image Upscale 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 Image Upscale 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/image/upscale for AI agents and LLMs