Topaz Image Enhance
Playground
Try it on WavespeedAI!Topaz Image Enhance improves images by removing noise, sharpening, and upscaling while preserving the details including faces of important subjects. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Topaz Image Enhance
Topaz Image Enhance is a professional-grade AI upscaling model powered by Topaz Labs’ technology. Upload your image, set target dimensions, and let AI intelligently upscale while adding detail and clarity — perfect for enlarging photos for print, display, or production use.
Why Choose This?
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AI-powered upscaling Intelligently enlarges images while adding realistic detail, not just interpolation.
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Multiple specialized models 5 models optimized for different image types — photos, low-res sources, CGI, high-fidelity, and text.
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Custom output sizing Set exact width and height for your target dimensions.
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Crop to fill option Optionally crop the image to exactly fill the specified dimensions.
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Professional quality Powered by Topaz Labs’ AI, trusted by professional photographers and studios worldwide.
Parameters
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source image to enhance (upload or URL) |
| width | No | Output width in pixels (default: 1080) |
| height | No | Output height in pixels (default: 1080) |
| model | No | Enhancement model to use (default: Standard V2) |
| output_format | No | Output format: jpeg, jpg, or png |
| crop_to_fill | No | Crop the image to fill the exact dimensions |
Model Options
| Model | Best For |
|---|---|
| Standard V2 | General-purpose upscaling for most photos (default) |
| Low Resolution V2 | Upscaling very low-resolution or small images |
| CGI | Computer-generated images, renders, and digital art |
| High Fidelity V2 | Maximum detail preservation for high-quality sources |
| Text Refine | Images containing text, documents, screenshots |
Output Format Options
- jpeg / jpg — Compressed format, smaller file size
- png — Lossless format, supports transparency
How to Use
- Upload your image — drag and drop or paste a URL.
- Set output size — specify target width and height.
- Select model — choose based on your image type.
- Enable crop to fill (optional) — check if you need exact dimensions.
- Choose output format — select based on your quality needs.
- Run — submit and download the enhanced image.
Pricing
| Item | Cost |
|---|---|
| Per image | $0.07 |
Simple flat-rate pricing regardless of image size or model selected.
Best Use Cases
- Print Enlargement — Upscale photos for large format printing.
- Low-Res Recovery — Enhance old or small images to usable sizes.
- CGI and Renders — Upscale 3D renders and digital artwork.
- Document Enhancement — Improve readability of scanned documents with text.
- Social Media — Upscale images for high-resolution display requirements.
Pro Tips
- Use Low Resolution V2 for very small or heavily compressed source images.
- Use CGI model for game screenshots, renders, and digital illustrations.
- Use Text Refine when your image contains important text or documents.
- High Fidelity V2 works best on already high-quality source images.
- Enable crop_to_fill when you need exact dimensions for a specific layout.
Notes
- Larger upscaling ratios may take longer to process.
- For best results, start with the highest quality source available.
- Different models are optimized for different content — experiment to find the best match.
Related Models
- Topaz Image Sharpen — AI-powered image sharpening.
- Topaz Image Denoise — AI-powered noise reduction.
- Topaz Image Lighting — AI-powered lighting adjustment.
- Topaz Image Restore — AI-powered dust and scratch removal.
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/enhance" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"size": "1080*1080",
"model": "Standard V2",
"output_format": "jpeg",
"crop_to_fill": false,
"enable_base64_output": false
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| image | string | No | - | The positive prompt for the generation.The image file to be processed. Supported formats (png jpg jpeg) | |
| size | string | No | 1080*1080 | 1 ~ 32000 per dimension | Specify the width and height pixel values of the generated image.Total pixel value range: [2560*1440, 4096*4096] |
| model | string | No | Standard V2 | Standard V2, Low Resolution V2, CGI, High Fidelity V2, Text Refine | The sharpen model to use. |
| output_format | string | No | jpeg | jpeg, jpg, png | The format of the output image. |
| crop_to_fill | boolean | No | false | - | Crop the image to fill the dimensions. |
| enable_base64_output | boolean | No | false | - | 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
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |