Omost Project Description

What is the project about?

Omost is a project that aims to leverage the coding capabilities of Large Language Models (LLMs) to generate images. It does this by having LLMs write code that composes image content using a virtual "Canvas" agent. This canvas can then be rendered by image generators to produce the final images.

What problem does it solve?

Omost addresses the challenge of bridging the gap between LLMs' text-based understanding and the visual domain of image generation. It allows users to generate images with more control over the composition and arrangement of elements within the image, going beyond simple text-to-image generation. It allows for more complex scene descriptions and object placement.

What are the features of the project?

LLM-Driven Image Composition: Uses LLMs to write code that defines image content and layout.
Virtual Canvas Agent: Provides a structured way for LLMs to describe image elements and their spatial relationships.
Multiple Pretrained Models: Offers three pretrained LLM models based on Llama3 and Phi3.
Conversational Editing: Allows users to iteratively refine image generation through dialogue with the LLM.
Structured Output: LLMs generate code with well-defined symbols and parameters for image composition.
Sub-prompt Handling: Uses "sub-prompts" to ensure lossless text encoding and coherent image generation.
Region-Guided Diffusion: Implements a baseline renderer based on attention manipulation for precise control over image regions.
Prompt Prefix Tree: Improves prompt understanding by organizing sub-prompts in a hierarchical structure.
HuggingFace Space and Local Deployment.

What are the technologies used in the project?

Large Language Models (LLMs): Llama3 and Phi3 variations.
Python: Programming language for the core logic and code generation.
PyTorch: Deep learning framework.
Diffusion Models: Underlying image generation technology.
Gradio: For creating a web-based user interface.
Hugging Face Transformers: For working with LLMs.
Bitsandbytes (optional): For quantized LLM inference.

What are the benefits of the project?

Enhanced Control: Provides finer control over image composition compared to standard text-to-image models.
Structured Generation: Uses a well-defined "Canvas" abstraction for predictable image layout.
Iterative Refinement: Supports conversational editing for interactive image creation.
Lossless Text Encoding: Employs "sub-prompts" to avoid semantic truncation issues.
Open Source: The project is available on GitHub, allowing for community contributions and extensions.
Efficient: Quantized models can run on GPUs with 8GB VRAM.

What are the use cases of the project?

Creative Image Generation: Creating complex scenes with specific object arrangements.
Visual Storytelling: Generating images that depict narratives with detailed descriptions.
Image Editing: Modifying existing images by adding, removing, or rearranging elements.
Prototyping and Design: Quickly visualizing concepts and ideas.
Research: Exploring the intersection of LLMs and image generation.
Educational Tool: Demonstrating how LLMs can be used for visual tasks.