# Pruna | P-Image | Upscale

p-image-upscale increases image resolution while enhancing detail and realism, sharpening photos and AI-generated visuals for crisp, high-resolution output.

## API Information

- **Model Slug:** pruna-p-image-upscale
- **Branded URL:** https://www.eachlabs.ai/pruna/p-image/pruna-p-image-upscale
- **Provider:** Pruna AI
- **Category:** Image to Image
- **Output Type:** image
- **Status:** active
- **Version:** 0.0.1
- **Base Cost:** 1-4 MP output target
- **Estimated Processing Time:** 8 seconds
- **Last Updated:** 2026-06-10
- **Interactive Demo:** https://www.eachlabs.ai/ai-models/pruna-p-image-upscale

## Pricing

- **Charge Type:** dynamic
- **Estimated Price (default example):** $0.005000
- **Pricing Details:** 1-4 MP output target

### Pricing Rules

| Condition | Pricing |
| --- | --- |
| target <= "4" | 1-4 MP output target |
| target <= "8" | 5-8 MP output target |
| target <= "16" | 9-16 MP output target |
| target <= "32" | 17-32 MP output target |
| target <= "64" | 33-64 MP output target |
| Rule 6 | 65-128 MP output target |

## Input Schema

No input parameters documented.

## Example Request

```bash
curl -X POST https://api.eachlabs.ai/v1/prediction/ \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "pruna-p-image-upscale",
  "input": {}
}'
```

## Output Schema

Response returned by `GET /v1/prediction/{id}` when the job completes:

```json
{
  "status": "success",
  "predictionID": "string",
  "output": "string (URL of generated image)",
  "metrics": {
    "predict_time": "number (seconds)"
  }
}
```

## Polling

```bash
curl https://api.eachlabs.ai/v1/prediction/{PREDICTION_ID} \
  -H "X-API-Key: YOUR_API_KEY"
```

| Status | Meaning |
|--------|---------|
| `processing` | Still running — poll again |
| `success` | Done — read `output` |
| `error` | Failed — read `message` / `details` |

## Webhook (alternative to polling)

Pass `"webhook_url": "https://your.host/path"` in the create request. Eachlabs POSTs this payload when the job ends:

```json
{
  "exec_id": "prediction-uuid",
  "status": "succeeded",
  "output": "https://...",
  "error": ""
}
```

`status` is `"succeeded"` or `"failed"`. `exec_id` equals the `predictionID` from create. Return 2xx within 30 seconds.

## Errors

Error body: `{ "status": "error", "message": "...", "details": "..." }`

| Code | Meaning |
|------|---------|
| `400` | Invalid input |
| `401` | Missing / invalid `X-API-Key` |
| `404` | Unknown model or prediction id |
| `429` | Rate limit — 100 creates / min, 10 concurrent per key |
| `5xx` | Retry with backoff |

## Overview

**Pruna | P-Image | Upscale Overview**

Pruna | P-Image | Upscale is an image-to-image enhancement model from **Pruna AI**, built to increase image resolution while preserving and refining visual detail. It focuses on sharpening textures, edges, and small features so that both photos and AI-generated images look crisp at higher sizes. Within the Pruna P-Image family, this variant is tailored for upscaling rather than creative style changes, making it ideal when you want a cleaner, higher-resolution version of an existing asset without radically altering its look. Integrated on each::labs, Pruna | P-Image | Upscale helps creators, designers, and developers turn low- or mid-resolution inputs into print-ready, presentation-ready, and web-ready outputs with minimal manual retouching.

## Usage Notes

- API Base URL: `https://api.eachlabs.ai/v1`
- Authentication: send `X-API-Key: YOUR_API_KEY`. Generate a key from the Eachlabs dashboard at https://www.eachlabs.ai/dashboard/api-keys.
- File-typed parameters (`*_url`, `image_url`, `video_url`, `audio_url`, etc.) accept publicly-reachable HTTPS URLs only. Upload your asset first (GCS / S3 / your CDN) and pass the resulting URL. Data-URIs and localhost URLs are rejected.
- For structured parameters (arrays / objects) send real JSON values, not stringified payloads.
- Monetary values are reported in USD; per-token / per-megapixel rates may be billed in micro-cents internally.
- Prefer `webhook_url` over polling for long-running predictions — see the Webhook Callback section.