Topaz Labs' Wonder 4 Fixes and Enlarges Photos in One Smart Pass
Topaz Labs ships Wonder 4, a mixture-of-experts image model that upscales, denoises, sharpens, deblurs, and recovers faces in a single pass.
- Topaz Labs released Wonder 4, a one-click upscale, denoise, and sharpen model.
- First Topaz model built on a mixture-of-experts architecture that routes enhancements per image region.
- Combines upscaling, denoising, deblurring, compression artifact removal, and face recovery in a single pass.
- Available now in Topaz for Web and Topaz for Mobile, with plans from $12/mo.
- Already live on Runware from $0.0428 per image, up to 256 MP and 6x upscale.
- Integrated into ComfyUI as a drop-in switch from Wonder 3.5 with identical pricing.
Topaz Labs has released Wonder 4, a one-click model that combines image enlargement and restoration without separate controls for each repair. Its mixture-of-experts architecture assigns regions of an image to specialized networks, aiming to reduce compounded artifacts and simplify workflows that previously required several models.
Routing repairs by region
Conventional restoration workflows often chain a denoiser, deblurring model, sharpener, and upscaler. Each stage receives the previous model’s output, creating opportunities for noise, halos, invented texture, and other errors to accumulate. According to Topaz, Wonder 4 divides an image into thousands of regions and selects the relevant specialists for each one. A face, smooth sky, blurred subject, and blocky text can therefore receive different processing within the same pass.
In machine-learning terms, a mixture-of-experts model uses a routing layer to choose specialized subnetworks for each input patch. Only the selected experts process that patch. This local routing gives the model a way to sharpen detailed subjects while applying gentler treatment to clean backgrounds or low-texture areas.
Five tasks share one pass
Wonder 4 combines five restoration tasks that commonly require separate tools:
- Upscaling from the original resolution
- Noise reduction
- Deblurring and sharpening
- Compression-artifact removal
- Facial-detail recovery
Wonder 3.5 focused on generative upscaling, while Wonder 4 expands the workflow to cover enlargement and repair together. The model provides no controls for tuning individual restoration operations. Host services still expose choices such as enlargement scale, output format, and export size.
From browser to API
At launch, Wonder 4 is available through Topaz’s web and mobile apps, Runware, and ComfyUI. Pricing and output limits vary by route.
| Route | Scale and limits | Price or setup |
|---|---|---|
| Topaz for Web and Topaz for Mobile | The Pro plan supports exports up to 256 megapixels (MP). | The Image plan starts at $12 per month. Pro costs $35 per month and includes full commercial use. |
| Runware | Supports 1x cleanup and enlargement up to 6x. Inputs and outputs can reach 256 MP and 32,000 pixels per side. Output formats include JPG, PNG, and WebP. | Pricing starts at $0.0428 per image. The model ID is topazlabs:wonder@4. |
| ComfyUI | Supports scales from 1x to 6x, with a maximum output of 179 MP. | Billing matches the Wonder 3.5 rate per 8 MP processed. Existing workflows can switch the model field on the current node. |
HDR arrives alongside Wonder
The release also adds a separate Image HDR model to the web and mobile apps. It converts standard photos into HDR JPEGs with a wider highlight and contrast range. The HDR conversion runs as its own model, separate from Wonder 4’s enlargement and restoration pass.
Best-fit workflows and limits
- Batch photography: Cleaning, sharpening, and enlarging a shoot without tuning every image.
- Photo restoration: Processing scans or compressed files that contain blur, noise, and JPEG artifacts in the same frame.
- Generative-image pipelines: Enlarging outputs from tools such as Midjourney or Sora while applying repairs selectively across the frame.
- Product integrations: Adding restoration through Runware or a ComfyUI workflow with explicit scale and export limits.
Zero-setting restoration provides limited control over grain, texture, sharpening strength, and face reconstruction. Teams with strict fidelity requirements should inspect faces, text, film grain, and repeated patterns at full resolution, then compare the results with Wonder 3.5 or Topaz’s specialist models. API integrations should also validate pixel dimensions before submission and account for each provider’s billing unit.
Comments
