Clarity AI Flux Upscaler sharpens images while preserving natural textures and edges, with prompt-guided refinement and LoRA support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.2每次運行·~50 / $10





Clarity AI Flux Upscaler enhances and enlarges images with prompt-guided refinement, optional LoRA style control, and adjustable creativity. It is designed for high-quality super-resolution workflows where you want both sharper detail and more control over the final look.
Prompt-guided upscaling Use a text prompt to steer texture, tone, lighting, or style during enhancement.
Optional LoRA control
Add a compatible lora_link when you want stronger domain-specific styling.
Megapixel-based output sizing Choose the target output size directly in megapixels for predictable delivery.
Flexible enhancement strength
Adjust creativity to balance faithful restoration against more generative detail.
Production-ready workflow Suitable for portraits, fashion, product imagery, artwork, and other high-resolution creative assets.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Input image to upscale. |
| target_megapixels | No | Target output size in megapixels. Higher values produce larger and more detailed outputs. |
| prompt | No | Optional prompt to guide tone, texture, lighting, or visual refinement. |
| lora_link | No | Optional URL to a compatible LoRA for additional style control. |
| creativity | No | Controls how much new detail is added. Lower values stay closer to the source, while higher values add stronger enhancement. |
lora_link if you want extra style steering.Elegant editorial texture, soft natural light, refined fabric detail, realistic skin tones, premium fashion photography look
Pricing is based on the selected target_megapixels tier.
| Target Megapixels | Cost |
|---|---|
<= 4 MP | $0.20 |
> 4 MP and <= 8 MP | $0.40 |
> 8 MP and <= 16 MP | $0.60 |
> 16 MP and <= 25 MP | $1.20 |
> 25 MP and <= 50 MP | $2.40 |
> 50 MP | $3.20 |
target_megapixelsprompt, lora_link, and creativity do not affect pricingcreativity lower when identity, structure, and source fidelity matter most.lora_link only when you need a stronger style direction.image is the only required field.target_megapixels tier.prompt, lora_link, and creativity change the look of the result, but not the price.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/clarity-ai/flux-upscaler with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Flux Upscaler below.
set -euo pipefail
: "${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/flux-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/flux-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/flux-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)Flux Upscaler is a Clarity model for upscaling, exposed as a REST API on WaveSpeedAI. Clarity AI Flux Upscaler sharpens images while preserving natural textures and edges, with prompt-guided refinement and LoRA support. 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-flux-upscaler.
Flux Upscaler starts at $0.20 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-flux-upscaler.
Median end-to-end generation time on WaveSpeedAI is around 106 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.