This face upscaler invents pixels instead of recovering them

What it does Given a low-resolution face image, PULSE hunts through the output space of a pretrained StyleGAN generator for a high-resolution face that looks realistic and, when downscaled, matches your input. The authors explicitly warn that it synthesizes imaginary faces of people who do not exist; it will not reconstruct or identify individuals from blurred photos.

The interesting bit Rather than regressing missing pixels with a CNN, PULSE frames super-resolution as an optimization problem over latent vectors. Every output is inherently plausible because it is generated by StyleGAN, but the fine details are invented, not restored.

Key highlights

• Searches StyleGAN outputs for high-res images that downscale correctly to the input
• Explicitly generates synthetic faces rather than reconstructing real identities
• Ships with align_face.py for face alignment and downscaling preprocessing
• Includes a model card and paper section addressing bias concerns
• Requires a CUDA-capable GPU and has been tested on Linux and Windows

Caveats

• Raw photos must be aligned and downscaled before processing; the tool does not ingest arbitrary images directly
• The pretrained model downloads from a public Google Drive link that occasionally hits capacity limits
• Windows builds may require hand-tweaking locked dependency hashes in the environment specification

Verdict A solid reference if you study generative super-resolution or latent-space optimization. Avoid it if you need faithful reconstruction of real identities or a CPU-only pipeline.