ControlNeXt

Why we included this project

Diffusion pipelines are easy to point with words but harder to steer precisely, and ControlNeXt is a lightweight control adapter built for that problem. It hooks into SDXL and SD1.5 for images and SVD for video, accepting structured conditions such as Canny edges, depth maps, masks, or pose sequences. The method cuts trainable parameters by roughly 90% compared with ControlNet, so fine-tuning on your own data converges faster, and it pairs with LoRA to change style without throwing the generation off course. The repo bundles trained adapters plus separate training scripts for the image and pose-driven video variants, which makes it useful both as a drop-in inference recipe and as a reference when you retrain. Teams working on controllable image editing or human-pose-animated video will also appreciate the shared training experiences and issue threads, which save a lot of trial and error.