How to upscale images with an API: increase resolution for print and display

LetsEnhance -

A customer uploads a 500px image. Your print partner needs 4000px at 300 DPI. Traditional resizing just makes it blurry. Rejecting the upload loses the sale.

An image upscaler API solves this by using AI to enlarge images while adding realistic detail. The result: higher resolution without the blur that comes from simple interpolation.

This guide covers how AI upscaling works, which model to choose for your use case, and how to integrate Claid API by LetsEnhance into your workflow.

What does an image upscaler API do?

AI upscaling (also called super-resolution) increases image dimensions by generating new pixels based on learned patterns. Unlike bicubic or bilinear interpolation, which just blend existing pixels, neural networks reconstruct plausible detail. Edges stay sharp, textures remain coherent, and faces look natural.

The practical outcome: you can take a small web image and produce a version suitable for large displays, print production, or high-DPI screens.

Claid API supports enlargement up to 16x and output sizes up to 512 megapixels, enough for large-format printing and commercial applications.

Common use cases

Print production typically requires 300 DPI. A 1000x1000px image at 72 DPI only prints at about 3.5 inches. Upscaling to 4000x4000px gives you a 13-inch print at full quality.

Printify integrated Claid API to automatically convert customer uploads to print-ready resolution. Previously, 15% of merchants worked with images that weren’t suitable for printing. Now the platform handles the conversion automatically, improving both quality and conversion rates.

Mixtiles uses the same approach for photo tiles. Customers upload phone photos, and the API ensures they print well on physical products.

Scaling AI-generated art

AI image generators like Midjourney and Stable Diffusion typically output at 1024x1024 or similar. For wall art, merchandise, or professional use, you need higher resolution.

The digital_art upscaling model is trained specifically for illustrations, paintings, and AI-generated imagery. Mirage Gallery uses it to upscale digital artworks to 8K for gallery prints.

Marketplace and e-commerce catalogs

Product catalogs often contain images from different sources, some high-res, some not. Upscaling standardizes everything to a consistent resolution, improving the browsing experience and enabling features like zoom.

Digital asset management

Media libraries accumulate images at various resolutions over years. Batch upscaling creates a high-resolution source of truth, making old assets usable for new channels without re-shooting.

How to upscale images using Claid API

Claid API provides several AI models optimized for different content types.

Step 1: get your API key

Sign up at Claid.ai for 50 free credits. Your API key is in the dashboard.

Step 2: choose the right upscaling model

Different AI models work better for different content. The API offers these options under restorations:

Model Best for
smart_enhance Product photos, real estate, food images, especially smaller, lower-quality originals
smart_resize High-quality images with readable text
photo General photography (people, nature, architecture) from phones or digital cameras
faces Portraits and images with people as the main subject
digital_art Illustrations, paintings, cartoons, anime, AI-generated art

The model choice matters. Using photo on a cartoon will produce worse results than digital_art. Using smart_enhance on a high-quality photo might over-process it, while smart_resize would preserve more original detail.

Step 3: set your target size

You can specify the output size in several ways using the resizing parameters:

  • Percentage: "width": "200%" doubles the width
  • Pixels: "width": 4000 sets exact pixel dimensions
  • Auto aspect ratio: "width": 4000, "height": "auto" scales proportionally

For print, calculate pixels from your target print size: pixels = inches × DPI. A 10x10 inch print at 300 DPI needs 3000x3000 pixels.

Print size increases as pixel dimensions scale at 300 DPI.

Step 4: send your request

Here’s a Python example that upscales a product photo to 4x:

import requests

response = requests.post(
    "https://api.claid.ai/v1-beta1/image/edit",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "input": "https://example.com/product-photo.jpg",
        "operations": {
            "restorations": {"upscale": "smart_enhance"},
            "resizing": {"width": "400%", "height": "400%"}
        }
    }
)
print(response.json()["output"]["url"])
4× upscaling produces sharper, print-ready product images.

For AI-generated art to 8K, change the operations:

"operations": {
    "restorations": {"upscale": "digital_art"},
    "resizing": {"width": 7680, "height": "auto"}
}
Digital art model preserves illustration detail when scaling to high resolution.

For complete code examples including error handling and batch processing, see the Claid API documentation.

Step 5: handle batch processing

For high volumes, use the async API with webhooks. For enterprise volumes, connect your cloud storage (AWS S3, Google Cloud Storage) for direct processing without URL transfers.

Combining upscaling with enhancement

Upscaling works best on clean source images. If your originals have JPEG artifacts, noise, or blur, combine upscaling with enhancement operations:

"operations": {
    "restorations": {"upscale": "photo", "decompress": "auto"},
    "resizing": {"width": "300%", "height": "300%"},
    "adjustments": {"hdr": 100}
}

This removes compression artifacts, upscales with the photo model, and applies color correction, all in one API call.

You can also combine upscaling with background removal, smart crop, or outpainting to extend canvas with AI-generated content.

Upscaling vs. enhancement: when to use each

Goal Use
Make images bigger (more pixels) Upscaling
Fix quality without changing size Enhancement (decompress, polish)
Clean up and enlarge Both together
Enhancement fixes quality, while upscaling increases resolution for larger outputs.

If you just need sharper images at the same size, like cleaning up blurry user uploads, enhancement alone is faster and uses fewer credits. If you need bigger images for print or display, upscaling is required.

Pricing and getting started

Claid API uses credits based on output size:

  • Upscaling: 1-6 credits depending on output resolution
  • Free trial: 50 credits on signup
  • Paid plans: starting at $59 for 1,000 credits ($0.059/credit)

Volume discounts are available for larger plans. See pricing details.

The API documentation has complete references, SDKs, and integration guides. For enterprise needs (custom SLAs, dedicated support, high-volume pricing), contact sales.

FAQ

What’s the relationship between Claid API and Let’s Enhance?

Claid API is the API platform built by Let’s Enhance. LetsEnhance.io is the original web tool for individual users. Claid.ai is where businesses access the same AI technology via API for integration into their own products and workflows.

How much can I upscale an image?

Up to 16x enlargement, with output sizes up to 512 megapixels. For most use cases, 2x-4x produces excellent results. Higher factors work but depend on source quality. A sharp 1000px original upscales better than a blurry 200px one.

What’s the difference between an upscaler API and an enhancer API?

An upscaler API increases resolution by adding pixels. An enhancer API fixes quality problems (blur, noise, artifacts) without necessarily changing dimensions. You can use both together for maximum improvement.

Which upscaling model should I use?

Use smart_enhance for product/food photos, photo for general photography, faces for portraits, and digital_art for illustrations or AI-generated images. smart_resize works best on already-high-quality images with text.

How does this compare to Photoshop or Topaz for upscaling?

Desktop tools require manual work and don’t scale to batch processing easily. Topaz runs locally but needs capable hardware. Claid API provides similar AI upscaling quality as a cloud service: integrate once, process automatically at any volume.

Can I convert 72 DPI images to 300 DPI for print?

Yes. DPI is just metadata. What matters is pixel dimensions. Calculate your target: a 10-inch print at 300 DPI needs 3000 pixels. If your image is 1000px, upscale to 3000px.

How long does upscaling take?

1-10 seconds depending on input size and upscale factor. Use webhooks for batch jobs to avoid timeout issues.

What formats are supported?

Input: JPEG, PNG, WebP. Output: JPEG, PNG, or WebP with configurable quality.

Can I upscale AI-generated images from Midjourney or Stable Diffusion?

Yes. The digital_art model is specifically trained for this. It handles the patterns common in AI art well and scales to 8K and beyond for printing.

Can I combine upscaling with background changes?

Yes. Chain operations in one request: upscale, remove background, add new background, adjust colors, whatever your workflow needs.

What’s coming next for the API?

Prime is coming soon, a new model that enhances clarity and detail while preserving original textures like skin, fabric, and product labels. Ideal when authenticity matters as much as sharpness.

Where can I compare upscaler API options?

See our overview of best image upscaler APIs for comparisons.