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Topaz Image Lighting

Topaz Image Lighting

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Topaz Image Lighting adjusts and balances images to improve quality despite sub-optimal lighting. Fix exposure, white balance, and color temperature. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Topaz Image Lighting

Topaz Image Lighting is a professional-grade image lighting and color adjustment model powered by Topaz Labs’ AI technology. Upload your image and let AI automatically adjust lighting, correct white balance, or even colorize black-and-white photos.


Why Choose This?

  • AI-powered lighting adjustment Automatically enhance exposure, shadows, highlights, and overall lighting balance.

  • White balance correction Fix color casts and correct inaccurate white balance from any lighting condition.

  • Photo colorization Transform black-and-white photos into natural-looking color images.

  • Professional quality Powered by Topaz Labs’ AI, trusted by professional photographers worldwide.

  • Multiple output formats Export as JPEG, PNG, or TIFF based on your workflow needs.


Parameters

ParameterRequiredDescription
imageYesSource image to process (upload or URL)
modelNoProcessing model to use (default: Adjust)
output_formatNoOutput format: jpeg, jpg, png, tiff, or tif

Model Options

ModelDescription
AdjustGeneral lighting and exposure adjustment (default)
Adjust V2Improved lighting adjustment with better tonal range
White BalanceCorrect color casts and white balance issues
ColorizeAdd natural color to black-and-white images

Output Format Options

  • jpeg / jpg — Compressed format, smaller file size
  • png — Lossless format, supports transparency
  • tiff / tif — Professional format, highest quality preservation

How to Use

  1. Upload your image — drag and drop or paste a URL.
  2. Select model — choose based on what you want to achieve.
  3. Choose output format — select based on your quality and file size needs.
  4. Run — submit and download the processed image.

Pricing

ItemCost
Per image$0.07

Simple flat-rate pricing regardless of image size or model selected.


Best Use Cases

  • Exposure Correction — Fix underexposed or overexposed photos.
  • White Balance Fix — Correct color casts from artificial lighting.
  • Photo Restoration — Colorize old black-and-white family photos.
  • Batch Processing — Consistently adjust lighting across multiple images.
  • Professional Editing — Quick lighting fixes in professional workflows.

Pro Tips

  • Use Adjust V2 for better results on challenging lighting conditions.
  • White Balance model works best on images with obvious color casts.
  • Colorize model produces natural results but may need manual tweaking for historical accuracy.
  • For archival work, export as TIFF to preserve maximum quality.

Notes

  • Adjust models work on both color and black-and-white images.
  • Colorize model is specifically designed for black-and-white to color conversion.
  • Results may vary based on original image quality and lighting conditions.

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/topaz/image/lighting" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "model": "Adjust",
    "output_format": "jpeg",
    "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
imagestringNo-The image file to be processed. Supported formats (png jpg jpeg tiff tif)
modelstringNoAdjustAdjust, Adjust V2, White Balance, ColorizeThe lighting model to use. Adjust: Balance exposure and lighting. Adjust V2: Enhanced lighting adjustment. White Balance: Correct color temperature. Colorize: Add natural color to images.
output_formatstringNojpegjpeg, jpg, png, tiff, tifThe format of the output image.
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.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
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 (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
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