# alex-damian/pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

Repository: https://github.com/alex-damian/pulse
Canonical: https://ross.abutalabs.com/products/pulse
Language: Python
License Family: other
Last push: 2021-04-30T11:50:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2296, "days_push": 1951, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8023, forks 1476 (observed 2026-08-28T04:10:11.870890+00:00)

## What it is
PULSE is a Python research implementation of a CVPR 2020 paper that upscales low-resolution face photos by searching the latent space of a pretrained StyleGAN for high-resolution outputs that downscale to match the input. It produces realistic but imaginary faces, not reconstructions of the original person.

## Use cases
- upscale low-resolution face photos to high resolution
- generate realistic high-res faces from blurry images
- explore StyleGAN latent space for image super-resolution
- reproduce CVPR 2020 super-resolution paper results
- research on GAN-based image upsampling

## When to choose
- you need GAN-based face super-resolution for research or experimentation
- you have a CUDA GPU and want to explore StyleGAN latent space optimization
- you want perceptually realistic upscaled faces rather than pixel-accurate reconstruction

## When to avoid
- you need to identify or reconstruct the actual original person from a blurred image
- you need a production-ready, licensed, actively maintained tool
- you have no CUDA-capable GPU
- you need super-resolution for non-face imagery

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, deep-learning, artificial-intelligence
- platform: python, windows
- tags: super-resolution, stylegan, face-upsampling, generative-models, cvpr-2020, research-code, linux, gpu

## Member repositories
- alex-damian/pulse (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:11.870890+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:31:49.799720+00:00, confidence not recorded.
  - readme: https://github.com/alex-damian/pulse (fetched 2026-08-28T04:10:11.870890+00:00, sha 831fe3effae5)
- Data as of 2026-08-30T08:39:29.467469+00:00.