JIA-Lab-research/ControlNeXt

Health v2 · maintenance only

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Adoption not part of the score

1646 stars · 80 forks observed · 2026-08-28

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

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

library · maturity active

machine-learning deep-learning image-processing video-processing llm-training machine-learning deep-learning image-processing artificial-intelligence python stable-diffusion controlnet svd lora controllable-generation diffusion-models pose-controlled-video video gpu linux

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