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pruna-ai/

Pruna AI P-Image Upscale is a fast AI image upscaling model that enhances image resolution and improves visual detail. Ready-to-use REST inference API for product photos, portraits, design assets, e-commerce images, social media visuals, and image enhancement workflows with simple integration, no coldstarts, and affordable pricing.

upscaler
इनपुट

निष्क्रिय

$0.005प्रति रन·~200 / $1

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README

Pruna AI P-Image Upscale

Pruna AI P-Image Upscale enhances and enlarges images with a simple workflow built around target size selection and flexible output formatting. It is suitable for restoring old images, improving low-resolution assets, preparing sharper visuals for design or marketing, and generating cleaner outputs for downstream use.

Why Choose This?

  • Simple image upscaling Upload a single image and generate a higher-quality result with minimal configuration.

  • Target-based output control Use the target setting to choose the desired upscale level or output target.

  • Clean enhancement workflow Improve image clarity for photos, scans, product images, and other visual assets.

  • Flexible output format Export the upscaled image in a supported format such as png.

  • Affordable tiered pricing Uses a simple pricing structure based on the selected target tier.

Parameters

ParameterRequiredDescription
imageYesInput image to upscale.
targetNoTarget upscale setting or output target level. Higher values produce a larger or stronger upscale result. Supports values up to 128.
output_formatNoOutput image format, such as png.

How to Use

  1. Upload your image — provide the source image you want to enhance.
  2. Choose the target — select the upscale target that best matches your quality needs.
  3. Choose output format (optional) — select the format that best fits your workflow.
  4. Submit — run the model and download the upscaled image.

Example Use Case

Upscale an old street photograph to produce a cleaner, sharper version for archival, presentation, or creative reuse.

Pricing

Pricing is based on the selected target tier.

TargetCost
<= 4$0.005
> 4 and <= 8$0.010
> 8 and <= 16$0.020
> 16 and <= 32$0.040
> 32 and <= 64$0.060
> 64 and <= 128$0.120

Billing Rules

  • Requests with target <= 4 cost $0.005 per image
  • Requests with target > 4 and <= 8 cost $0.010 per image
  • Requests with target > 8 and <= 16 cost $0.020 per image
  • Requests with target > 16 and <= 32 cost $0.040 per image
  • Requests with target > 32 and <= 64 cost $0.060 per image
  • Requests with target > 64 and <= 128 cost $0.120 per image
  • Pricing depends on the selected target
  • output_format does not affect pricing

Best Use Cases

  • Old photo enhancement — Improve the clarity of scanned or low-resolution photographs.
  • Design asset preparation — Create sharper source images for layouts, presentations, and creative projects.
  • Product image improvement — Upscale commercial visuals for catalogs, ads, and marketplace listings.
  • Archival restoration workflows — Produce cleaner and larger outputs from legacy image assets.
  • General low-resolution cleanup — Improve images that need a simple boost in size and quality.

Pro Tips

  • Start with a lower target setting first if you want a faster and cheaper test run.
  • Use a clean source image whenever possible for better enhancement results.
  • Choose a higher target only when you actually need the larger or stronger upscale output.
  • png is a good choice when you want to preserve output quality.

Notes

  • image is the only required field.
  • target now supports values up to 128.
  • Pricing depends on the selected target tier.
  • output_format changes the file type, but not the price.
  • Higher target values may be more suitable for print, detailed review, or premium delivery workflows.

Related Models

नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है। दस्तावेज़ की कीमतें केवल संदर्भ के लिए हैं और पुरानी हो सकती हैं। Generate बटन अनुमान दिखाता है; टास्क का अंतिम शुल्क ही मान्य होगा।

P Image Upscale API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-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 P 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",
    "target": 4,
    "output_format": "png"
}
JSON
)

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

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/pruna-ai/p-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 = 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)

P Image Upscale API — Frequently asked questions

What is the P Image Upscale API?

P Image Upscale is a Pruna Ai model for upscaling, exposed as a REST API on WaveSpeedAI. Pruna AI P-Image Upscale is a fast AI image upscaling model that enhances image resolution and improves visual detail. Ready-to-use REST inference API for product photos, portraits, design assets, e-commerce images, social media visuals, and image enhancement workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the P 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 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/pruna-ai/pruna-ai-p-image-upscale.

How much does P Image Upscale cost per run?

P Image Upscale starts at $0.005 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.

What inputs does P Image Upscale accept?

Key inputs: `image`, `enable_base64_output`, `enable_sync_mode`, `output_format`, `target`. 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/pruna-ai/pruna-ai-p-image-upscale.

How long does P Image Upscale take to generate?

Median end-to-end generation time on WaveSpeedAI is around 15 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use P Image Upscale outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Pruna Ai). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

P Image Upscale | AI Image Upscaler API on WaveSpeedAI