Omost is an innovative project that harnesses the encoding capabilities of large language models (LLMs) and combines them with image generation, creating a groundbreaking image synthesis technology.
Key Features
Omost offers pre-trained LLM models based on Llama3 and Phi3 variants. These models can write code through a specific virtual “Canvas” agent to compose visual content for images, which are then generated by the actual implementation of the image generator.
Training Data
The models are trained using a mix of data, including:
- Real annotations from multiple datasets
- Extracted data from automatically annotated images
- Reinforcement from direct preference optimization (DPO)
- Fine-tuning data from OpenAI GPT4o’s multimodal capabilities
Accessibility
Omost provides an official HuggingFace space[2] as well as local deployment options, making it easy for users to start using the technology quickly.
Detailed Image Generation
Through a series of pre-trained models, Omost can generate highly detailed and dynamic images based on textual descriptions.
Applications
Omost can be used in various image generation scenarios, including but not limited to:
- Artistic creation
- Game design
- Advertising production
It helps users transform textual descriptions into visual images, thereby improving creative efficiency and innovation.
Getting Started
To start using Omost, you can either use the official HuggingFace space[2] or follow these deployment steps:
- Clone the Omost repository:
git clone https://github.com/lllyasviel/Omost.git- Navigate to the project directory:
cd Omost- Create and activate a Conda environment:
conda create -n omost python=3.10
conda activate omost- Install PyTorch and torchvision:
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121- Install project dependencies:
pip install -r requirements.txt- Run the Gradio application:
python gradio_app.pyModel Variants
Omost offers three pre-trained LLM models and their quantized versions to accommodate different hardware configurations and performance requirements.
Quantized Models
omost-llama-3-8b-4bitsomost-dolphin-2.9-llama3-8b-4bitsomost-phi-3-mini-128k-8bits
Non-quantized Models
omost-llama-3-8bomost-dolphin-2.9-llama3-8bomost-phi-3-mini-128k
Examples
A Ragged Man in the 19th Century Wearing a Tattered Jacket
Jurassic Dinosaur Battle
Related Research
The Omost project is associated with the following research:
- DOCCI: Descriptions of Connected and Contrasting Images
- RPG-DiffusionMaster: Mastering Text-to-Image Diffusion
- Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following
- LLM-grounded Diffusion: Enhancing Prompt Understanding
- Self-correcting LLM-controlled Diffusion Models
- MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Please note that the content in this article is for reference only. For the latest project features, please refer to the official GitHub page.
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