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Ominicontrol
Overview :
OminiControl is a minimal yet powerful universal control framework designed for diffusion transformer models like FLUX. It supports subject-driven control and spatial tasks (such as edge guidance and image restoration). The design of OminiControl is extremely streamlined, introducing only 0.1% additional parameters to the base model while retaining the original model structure. This project is developed by the Learning and Vision Laboratory at the National University of Singapore, representing the latest advancements in image generation and control technologies in the field of artificial intelligence.
Target Users :
The target audience includes researchers, developers, and AI enthusiasts, particularly those interested in image generation, image restoration, and deep learning technologies. OminiControl provides a flexible and powerful tool that allows users to generate and control images according to their needs, without requiring an in-depth understanding of complex deep learning models.
Use Cases
Use OminiControl to generate an image of a specific subject, such as 'a close-up view of an orange'.
Utilize the spatial control feature to repair damaged images, such as 'restoring a torn old photograph'.
Combine edge guidance functionality to create detailed images based on sketches, such as 'producing a landscape painting from a sketch'.
Features
Subject-driven control: Supports generating images based on subjects or conditions.
Spatial control: Capable of tasks such as edge guidance and image restoration.
Minimalist design: Introduces a minimal number of additional parameters while preserving the original model structure.
High compatibility: Compatible with diffusion transformer models like FLUX.
User-friendly: Offers detailed quick start guides and examples.
Flexible applications: Suitable for various applications including image generation and image restoration.
How to Use
1. Environment Setup: Create and activate a new virtual environment using conda.
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2. Install Dependencies: Install the necessary libraries and dependencies as specified in requirements.txt.
3. Download the Model: Obtain the pre-trained OminiControl model from Hugging Face or GitHub.
4. Prepare Data: Organize the input data required for the desired control tasks, such as subject images or spatial control signals.
5. Run Examples: Execute the Jupyter Notebooks located in the examples directory to view demonstrations of different features.
6. Customize Generation: Use the provided API and documentation to customize generation parameters to produce the desired images.
7. Evaluate Results: Check if the generated images meet your expectations and make necessary adjustments.
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