Browse ModelsPruna AIPruna AI P Image Upscale

Pruna Ai P Image Upscale

Pruna Ai P Image Upscale

Playground

Try it 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.

Features

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 pricing Uses a simple low-cost 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.
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$0.010

Billing Rules

  • Requests with target <= 4 cost $0.005 per image
  • Requests with target > 4 cost $0.010 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 the 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.
  • 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.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/upscale" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "target": 4,
    "output_format": "png",
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
imagestringYes-Input image URL.
targetintegerNo41, 2, 3, 4, 5, 6, 7, 8Target output size in megapixels.
output_formatstringNopngpng, jpg, webpOutput image format.
enable_sync_modebooleanNofalse-If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
enable_base64_outputbooleanNofalse-If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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