zsyOAOA/InvSR

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1443 stars · 93 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

InvSR is a Python research library implementing arbitrary-steps image super-resolution via diffusion inversion, leveraging pre-trained diffusion models with a partial noise prediction strategy. It supports flexible sampling from one to five steps and ships with Gradio, Hugging Face, Replicate, and Colab demos.

Use cases

• upscale low-resolution images with diffusion models
• one-step image super-resolution
• restore blurry or degraded photos to high resolution
• run super-resolution with a configurable number of sampling steps
• experiment with diffusion inversion for image restoration
• deploy an image upscaling demo on Hugging Face or Replicate

When to choose

• you need state-of-the-art diffusion-based super-resolution with fast, few-step sampling
• you want a research-grade SR model with ready-made online demos
• you have a GPU and want flexible control over the quality/speed trade-off

When to avoid

• you need a lightweight CPU-only upscaler
• you require a permissively licensed production library (license is non-standard)
• you only need simple classical upscaling like Lanczos or ESRGAN

Facets

library · maturity active

image-processing machine-learning deep-learning llm-inference image-processing computer-vision deep-learning artificial-intelligence python cross-platform super-resolution diffusion-models diffusion-inversion cvpr-2025 image-restoration research-code gradio-demo gpu linux

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