OminiControl

OminiControl: Minimal and Universal Control for Diffusion Transformer
Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang
Learning and Vision 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.

OminiControlGP (OminiControl for the GPU Poor) by DeepBeepMeep

With just one line adding the 'mmgp' module (https://github.com/deepbeepmeep/mmgp\), OminiControl can generate images from a derived Flux model in less than 6s with 16 GB of VRAM (profile 1), in 9s with 8 GB VRAM (profile 4) or in 16s with less than 6 GB of VRAM (profile 5)

To run the Gradio app with a profile 3 (default profile, the fastest but requires the most VRAM):

python gradio_app --profile 3

To run the Gradio app with a profile 5 (a bit slower but requires only 6 GB of VRAM):

python gradio_app --profile 5

You may check the mmgp git homepage if you want to design your own profiles (for instance to disable quantization).

If you enjoy this applcitation, you will certainly appreciate these ones:\

• Hunyuan3D-2GP: https://github.com/deepbeepmeep/Hunyuan3D-2GP\ A great image to 3D or text to 3D tool by the Tencent team. Thanks to mmgp it can run with less than 6 GB of VRAM

• HuanyuanVideoGP: https://github.com/deepbeepmeep/HunyuanVideoGP\ One of the best open source Text to Video generator

• FluxFillGP: https://github.com/deepbeepmeep/FluxFillGP\ One of the best inpainting / outpainting tools based on Flux that can run with less than 12 GB of VRAM.

• Cosmos1GP: https://github.com/deepbeepmeep/Cosmos1GP\ This application include two models: a text to world generator and a image / video to world (probably the best open source image to video generator).

News

• 2025-01-25: ⭐️ DeepBeepMeep fork: added support for mmgp
• 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=3.10
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.

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. (To be released)	(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)
experimental / <task_name>_1024	FLUX.1	Supports higher resolution.(To be released)	(1024, 1024)

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 currently 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).

Citation

@article{
  tan2024omini,
  title={OminiControl: Minimal and Universal Control for Diffusion Transformer},
  author={Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang},
  journal={arXiv preprint arXiv:2411.15098},
  year={2024}
}