These models increase image resolution and quality.

Read our guide to upscaling images with AI to learn about various upscaling models.

Key capabilities of upscaling models:

• Super-resolution - Upscale images by inferring high frequency details. Allows increasing resolution without losing quality.
• Noise reduction - Remove artifacts and imperfections during upscaling. Results in cleaner image.
• Face restoration - Improve facial features and textures for more natural faces.
• Control over style - Adjust degree of realism vs hallucination during upscaling.

Our Pick: batouresearch/magic-image-refiner

For most upscaling needs, we recommend the batouresearch/magic-image-refiner model. This very flexible model can be used for upscaling, refining an image, or inpainting. The model can upscale images to either 1024x1024px or 2048x2048px, producing great results much faster than comparable models.

Increase the resemblance parameter to get a more precise recreation of your original input image. Or, if you’re looking for something new and interesting, crank up the creativity parameter to encourage hallucination and create a new image inspired by your original input.

You may also be interested in the sister model, batouresearch/high-resolution-controlnet-tile, which upscales to a larger resolution of 2560x2560. However, it runs slower and produces less realistic-looking results.

Budget Pick: nightmareai/real-esrgan

If you need to upscale a large volume of images, we suggest using nightmareai/real-esrgan.

It runs fast on cheaper GPUs, like the Nvidia T4 (~1.8s for a 2x upscale), and produces reasonably good upscaled images without too many image scaling artifacts. Real-ESRGAN also includes an optional face_enhance option, which can help improve the quality and realism of AI-generated faces.

You can run Real-ESRGAN on an Nvidia A100 for faster upscaling speed (~0.7s for a 2x upscale) and 2.5x the amount of GPU RAM, allowing for significantly larger images. Just keep in mind that A100 GPUs cost more than T4 GPUs.