OminiControl

OminiControl: Minimal and Universal Control for Diffusion Transformer
Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang
xML Lab, National University of Singapore

OminiControl2: Efficient Conditioning for Diffusion Transformers
Zhenxiong Tan, Qiaochu Xue, Xingyi Yang, Songhua Liu, and Xinchao Wang
xML Lab, National University of Singapore

Features

OminiControl is a minimal yet powerful universal control framework for Diffusion Transformer models like FLUX.

• Universal Control 🌐: A unified control framework that supports both subject-driven control and spatial control (such as edge-guided and in-painting generation).

• Minimal Design 🚀: Injects control signals while preserving original model structure. Only introduces 0.1% additional parameters to the base model.

News

• 2025-05-12: ⭐️ The code of OminiControl2 is released. It introduces a new efficient conditioning method for diffusion transformers. (Check out the training code here).
• 2025-05-12: Support custom style LoRA. (Check out the example).
• 2025-04-09: ⭐️ OminiControl Art is released. It can stylize any image with a artistic style. (Check out the demo and inference examples).
• 2024-12-26: Training code are released. Now you can create your own OminiControl model by customizing any control tasks (3D, multi-view, pose-guided, try-on, etc.) with the FLUX model. Check the training folder for more details.

Quick Start

Setup (Optional)

1. Environment setup

conda create -n omini python=3.12
conda activate omini

2. Requirements installation

pip install -r requirements.txt

Usage example

1. Subject-driven generation: examples/subject.ipynb
2. In-painting: examples/inpainting.ipynb
3. Canny edge to image, depth to image, colorization, deblurring: examples/spatial.ipynb

Guidelines for subject-driven generation

1. Input images are automatically center-cropped and resized to 512x512 resolution.
2. When writing prompts, refer to the subject using phrases like this item, the object, or it. e.g.
   1. A close up view of this item. It is placed on a wooden table.
   2. A young lady is wearing this shirt.
3. The model primarily works with objects rather than human subjects currently, due to the absence of human data in training.

Generated samples

Subject-driven generation

Demos (Left: condition image; Right: generated image)

Text Prompts

• Prompt1: A close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show. With text on the screen that reads 'Omini Control!.'
• Prompt2: A film style shot. On the moon, this item drives across the moon surface. A flag on it reads 'Omini'. The background is that Earth looms large in the foreground.
• Prompt3: In a Bauhaus style room, this item is placed on a shiny glass table, with a vase of flowers next to it. In the afternoon sun, the shadows of the blinds are cast on the wall.
• Prompt4: "On the beach, a lady sits under a beach umbrella with 'Omini' written on it. She's wearing this shirt and has a big smile on her face, with her surfboard hehind her. The sun is setting in the background. The sky is a beautiful shade of orange and purple."

More results

• Try on:
• Scene variations:
• Dreambooth dataset:
• Oye-cartoon finetune:
  

Spatially aligned control

1. Image Inpainting (Left: original image; Center: masked image; Right: filled image)

• Prompt: The Mona Lisa is wearing a white VR headset with 'Omini' written on it.
  
• Prompt: A yellow book with the word 'OMINI' in large font on the cover. The text 'for FLUX' appears at the bottom.
  

2. Other spatially aligned tasks (Canny edge to image, depth to image, colorization, deblurring)
   

   Click to show

   

   Prompt: A light gray sofa stands against a white wall, featuring a black and white geometric patterned pillow. A white side table sits next to the sofa, topped with a white adjustable desk lamp and some books. Dark hardwood flooring contrasts with the pale walls and furniture.

Stylize images

Models

Subject-driven control:

Model	Base model	Description	Resolution
experimental / subject	FLUX.1-schnell	The model used in the paper.	(512, 512)
omini / subject_512	FLUX.1-schnell	The model has been fine-tuned on a larger dataset.	(512, 512)
omini / subject_1024	FLUX.1-schnell	The model has been fine-tuned on a larger dataset and accommodates higher resolution.	(1024, 1024)
oye-cartoon	FLUX.1-dev	The model has been fine-tuned on oye-cartoon dataset by @saquib764	(512, 512)

Spatial aligned control:

Model	Base model	Description	Resolution
experimental / <task_name>	FLUX.1	Canny edge to image, depth to image, colorization, deblurring, in-painting	(512, 512)

Community Extensions

• ComfyUI-Diffusers-OminiControl - ComfyUI integration by @Macoron
• ComfyUI_RH_OminiControl - ComfyUI integration by @HM-RunningHub

Limitations

1. The model's subject-driven generation primarily works with objects rather than human subjects due to the absence of human data in training.
2. The subject-driven generation model may not work well with FLUX.1-dev.
3. The released model only supports the resolution of 512x512.

Training

Training instructions can be found in this folder.

To-do

• Release the training code.
• Release the model for higher resolution (1024x1024).

Acknowledgment

We would like to acknowledge that the computational work involved in this research work is partially supported by NUS IT’s Research Computing group using grant numbers NUSREC-HPC-00001.

Citation

@article{tan2024ominicontrol,
  title={OminiControl: Minimal and Universal Control for Diffusion Transformer},
  author={Tan, Zhenxiong and Liu, Songhua and Yang, Xingyi and Xue, Qiaochu and Wang, Xinchao},
  journal={arXiv preprint arXiv:2411.15098},
  year={2024}
}

@article{tan2025ominicontrol2,
  title={OminiControl2: Efficient Conditioning for Diffusion Transformers},
  author={Tan, Zhenxiong and Xue, Qiaochu and Yang, Xingyi and Liu, Songhua and Wang, Xinchao},
  journal={arXiv preprint arXiv:2503.08280},
  year={2025}
}