Clarity AI Crystal Upscaler boosts image resolution with AI upscaling and adjustable detail for portraits and landscapes. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
就緒

$0.03每次運行·~33 / $1








Clarity AI Crystal Upscaler enhances and enlarges images with a megapixel-based output target and adjustable creativity control. It is designed for photo restoration, product image enhancement, print preparation, archival cleanup, and other workflows where you want a sharper, larger final image with predictable output sizing.
Megapixel-based output control
Choose the target output size directly in megapixels for more predictable delivery planning.
Clean high-resolution enhancement
Improve sharpness, detail, and overall clarity for photos, scans, product visuals, and creative assets.
Creativity adjustment
Use creativity to balance strict source preservation against more generated detail.
Simple workflow
Upload one image, choose the target megapixels, optionally adjust creativity, and generate the result.
Production-ready API
Suitable for restoration, publishing, e-commerce, design, and large-format image workflows.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Input image to upscale. |
| target_megapixels | No | Requested output size in megapixels. Range: 1–1500 MP. Default: 4. |
| creativity | No | Negative values stay stricter to the source; positive values add more generated detail. Range: 0 to 10. Default: 0. |
Upscale a product photo for print and marketing use while preserving clean edges, surface texture, and overall realism.
Pricing is based on the selected target_megapixels tier.
| Target Megapixels | Cost |
|---|---|
<= 4 MP | $0.05 |
> 4 MP and <= 16 MP | $0.20 |
> 16 MP and <= 36 MP | $0.80 |
> 36 MP and <= 100 MP | $1.60 |
> 100 MP and <= 400 MP | $6.40 |
> 400 MP | $24.00 |
target_megapixelscreativity does not affect pricingcreativity near 0 when you want a more natural, source-faithful result.creativity when preserving structure, faces, logos, or product accuracy matters most.creativity gradually when you want stronger generated texture or enhancement.image is required.target_megapixels supports values from 1 to 1500.creativity ranges from 0 to 10.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/clarity-ai/crystal-upscaler with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Crystal Upscaler below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"target_megapixels": 4,
"creativity": 0
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/clarity-ai/crystal-upscaler" \
-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=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/clarity-ai/crystal-upscaler";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"target_megapixels": 4,
"creativity": 0
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
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 = {
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"target_megapixels": 4,
"creativity": 0
}
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/clarity-ai/crystal-upscaler", 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 = task.get("urls", {}).get("get") or 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"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Crystal Upscaler is a Clarity model for upscaling, exposed as a REST API on WaveSpeedAI. Clarity AI Crystal Upscaler boosts image resolution with AI upscaling and adjustable detail for portraits and landscapes. 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 production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/clarity-ai/clarity-ai-crystal-upscaler.
Crystal Upscaler starts at $0.030 per run. 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: `image`, `creativity`, `target_megapixels`. 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/clarity-ai/clarity-ai-crystal-upscaler.
Median end-to-end generation time on WaveSpeedAI is around 30 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Clarity). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.