Emerald

At a glance

Emerald offers browser-based AI image enlargement for people who want to use their own GPU instead of sending a photograph to a cloud processing service. Its main attraction is local processing without an installation or subscription, according to the publisher. It is worth investigating if your browser and hardware can run the models. The cost in time starts with model downloads, which range from a few megabytes to nearly four gigabytes.

Good fit

Trying a larger version of a small image while controlling its final dimensions. The interface includes a supplied before-and-after comparison and a sample-image option, while the processing guide distinguishes smaller Real-ESRGAN restoration models from the larger VOSR2 diffusion path. That gives you a sensible way to start small before committing to a heavy download.

Consider something else when

You need predictable batch throughput, a guarantee that enlarged detail is faithful to the original, or a browser-independent fallback on weak hardware. A generative upscaler predicts plausible texture rather than recovering information that was never captured. The publisher says the diffusion path requires a real GPU with half-precision shader support and has no CPU fallback. An ordinary resize may be enough when extra pixels matter more than invented detail.

Start here

Read How it works, then return to the upscaler and inspect the supplied comparison or Try Sample Image before using a personal photo. Choose a small Real-ESRGAN variant first, check the required download and pick a modest target size. Compare the result at actual pixels, especially text, faces and edges, before saving it.

Cost and requirements

Checked October 31, 2026, the publisher presents Emerald as free, without an account or subscription. Compute uses your hardware. Its documentation lists Real-ESRGAN assets of roughly 2.50–67.08 MB and a 3.89 GB VOSR2 bundle, cached by the browser after download. Inputs are JPEG, PNG or WebP, limited to 32 MiB and 24 megapixels, with a separate edge limit. Large-output support and a ready-GPU indicator do not establish that your machine can finish a particular run.

Limits to know

The interface and detailed processing explanation were inspected, not runtime image quality, download integrity or network behavior. The no-upload claim remains the publisher’s account of its design. The guide warns that large output caps are geometric limits rather than hardware promises, that enlargement beyond a model’s native scale can involve ordinary resizing, and that an optional second diffusion pass can amplify artifacts. A larger or sharper-looking file is not proof that its new detail is correct.

Still unclear

• How well does a representative image hold up on the reader’s GPU at the intended output size?
• Does the quality improvement over ordinary resizing justify the download, processing time and potential artifacts?