# JIA-Lab-research/ControlNeXt

Controllable video and image Generation, SVD, Animate Anyone, ControlNet, ControlNeXt, LoRA

Repository: https://github.com/JIA-Lab-research/ControlNeXt
Canonical: https://ross.abutalabs.com/products/controlnext
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-09-25T07:45:41+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 58
- inputs: {"age_days": 812, "days_push": 707, "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 1646, forks 80 (observed 2026-08-28T04:05:16.360937+00:00)

## What it is
ControlNeXt is the official implementation of a controllable generation method for images and videos, built on Stable Diffusion XL, Stable Diffusion 1.5, and Stable Video Diffusion. It reduces trainable parameters by up to 90% compared to ControlNet, converges faster, and integrates with LoRA for style control.

## Use cases
- generate videos controlled by human pose sequences
- controllable image generation with stable diffusion
- train a lightweight ControlNet alternative
- replicate Animate Anyone with SVD
- combine pose control with LoRA styles
- train controllable video generation models

## When to choose
- you need pose- or condition-controlled image/video generation with fewer trainable parameters
- you want faster convergence than ControlNet and LoRA compatibility
- you want training scripts plus pretrained models for SD1.5, SDXL, and SVD

## When to avoid
- you need a production-ready, stable API since the project is still under iterative development
- you lack GPU resources for SVD-based video generation
- you need non-diffusion generative models

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, video-processing, llm-training
- domain: machine-learning, deep-learning, image-processing, artificial-intelligence
- platform: python
- tags: stable-diffusion, controlnet, svd, lora, controllable-generation, diffusion-models, pose-controlled-video, video, gpu, linux

## Member repositories
- JIA-Lab-research/ControlNeXt (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.360937+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 2026-08-30T03:45:22.076442+00:00, confidence not recorded.
  - readme: https://github.com/JIA-Lab-research/ControlNeXt (fetched 2026-08-28T04:05:16.360937+00:00, sha 808b51361de2)
- Data as of 2026-08-30T08:39:29.467469+00:00.