# xinsir6/ControlNetPlus

ControlNet++: All-in-one ControlNet for image generations and editing!

Repository: https://github.com/xinsir6/ControlNetPlus
Canonical: https://ross.abutalabs.com/products/controlnetplus
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-09-30T13:58:40+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 56
- inputs: {"age_days": 792, "days_push": 702, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2138, forks 64 (observed 2026-08-28T04:06:18.157620+00:00)

## What it is
ControlNet++ is an all-in-one ControlNet model and architecture supporting 10+ control types for text-to-image generation and image editing, built on SDXL. It extends the original ControlNet with new modules that share a single condition encoder across multiple image conditions without extra computation.

## Use cases
- generate images with multiple control conditions
- deblur images with diffusion models
- upscale images to high resolution
- edit images with fine-grained control
- control stable diffusion image generation
- super resolution image enhancement

## When to choose
- you need multi-condition control over SDXL image generation
- you want tile-based deblur, variation, or super-resolution editing
- you need high-resolution outputs comparable to midjourney

## When to avoid
- you need SD3 support, which is paused pending GPU resources
- you need a plug-and-play inference pipeline rather than model weights and integration code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, image-processing, stable-diffusion
- domain: artificial-intelligence, image-processing, deep-learning
- platform: python
- tags: controlnet, text-to-image, sdxl, diffusion-models, image-editing, model-weights, gpu

## Member repositories
- xinsir6/ControlNetPlus (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:18.157620+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 2026-08-30T02:51:41.586451+00:00, confidence not recorded.
  - readme: https://github.com/xinsir6/ControlNetPlus (fetched 2026-08-28T04:06:18.157620+00:00, sha 90df0236fc27)
- Data as of 2026-08-30T08:39:29.467469+00:00.