RelaCtrl

This is the official reproduction of RelaCtrl, which represents an efficient controlnet-like architecture designed for DiTs.

RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers
Ke Cao*, Jing Wang*, Ao Ma*, Jiasong Feng, Zhanjie Zhang, Xuanhua He, Shanyuan Liu, Bo Cheng, Dawei Leng‡, Yuhui Yin, Jie Zhang‡(*Equal Contribution, ‡Corresponding Authors)

📰 News

• [2025.04.07] We released the inference pipeline and some weights of RelaCtrl-PixArt.
• [2025.02.21] We have released our paper RelaCtrl and created a dedicated project homepage.

Inference with RealCtrl on PixArt

Dependencies and Installation

conda create -n relactrl python=3.10
conda activate relactrl

pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118

git clone https://github.com/360CVGroup/RelaCtrl.git
cd RelaCtrl
pip install -r requirements.txt

Download Models

1. Required PixArt-related Weights

Download the necessary model weights for PixArt from the links below:

Model	Parameters	Download Link
T5	4.3B	T5
VAE	80M	VAE
PixArt-α-1024	0.6B	PixArt-XL-2-1024-MS.pth or Diffusers Version

2. RelaCtrl Conditional Weights

Download the required conditional weights for RelaCtrl:

Model	Parameters	Download Link
RelaCtrl_PixArt_Canny	45M	Canny
RelaCtrl_PixArt_Canny_Style	45M	Style

Inference with Conditions

python pipeline/test_relactrl_pixart_1024.py diffusion/configs/config_relactrl_pixart_1024.py

Prompt examples for different models can be found in the prompt_exampeles.

Acknowledgment

The PixArt model weights are derived from the open-source project PixArt-alpha.
Please refer to the original repository for detailed license information.

BibTeX

@misc{cao2025relactrl,
                title={RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers}, 
                author={Ke Cao and Jing Wang and Ao Ma and Jiasong Feng and Zhanjie Zhang and Xuanhua He and Shanyuan Liu and Bo Cheng and Dawei Leng and Yuhui Yin and Jie Zhang},
                year={2025},
                eprint={2502.14377},
                archivePrefix={arXiv},
                primaryClass={cs.CV},
                url={https://arxiv.org/abs/2502.14377}, 
}