Last updated 07-01-2026
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Omost
Omost is an open-source research project that turns large language model coding skills into structured image composition. You describe a scene in plain language, and a fine-tuned LLM writes Python that lays out global and regional elements on a virtual Canvas. A diffusion renderer then turns that layout into a finished image.
The approach treats scene building like programming: the model calls methods such as set_global_description and add_local_description to place subjects with location, size, depth, and color hints. That structure gives diffusion models region-aware guidance instead of relying on a single flat prompt.
You can run Omost locally with a Gradio app or try the hosted demo linked from the repository. It targets researchers, ML engineers, and image generation developers who want finer control over multi-subject compositions in Stable Diffusion-style pipelines.
Fine-tuned LLMs write Python Canvas code from your text prompt
Place subjects with location, offset, area, depth, and HTML color names
Swap elements conversationally, like changing a dragon to a dinosaur mid-session
Three pretrained model families based on Llama 3 and Phi-3 variants
Ships with a baseline region-aware diffusion renderer using attention manipulation
Run locally via Gradio with 8GB Nvidia VRAM, or use the linked HuggingFace space
Open source under Apache 2.0 with full local deployment instructions on GitHub.
Region-aware Canvas code gives finer multi-subject control than a single text prompt.
Conversational editing lets you refine compositions before rendering.
Quantized model variants fit in 8GB VRAM without offloading.
Local setup requires an Nvidia GPU with at least 8GB VRAM.
Repository activity slowed after mid-2024; last main-branch commits are about two years old.
The linked HuggingFace demo space may fail to load due to dependency version mismatches.
Is Omost free to use?
Yes. Omost is open source under the Apache 2.0 license on GitHub. You can clone the repository, run it locally, or use the official HuggingFace space linked from the README at no cost.
What hardware does Omost need for local runs?
Local deployment requires about 8GB of Nvidia VRAM. The README recommends Python 3.10, PyTorch with CUDA 12.1, and the pinned dependencies in requirements.txt. Quantized models are provided for GPUs with limited memory.
Which LLM models does Omost support?
Omost provides three pretrained model families on HuggingFace: omost-llama-3-8b, omost-dolphin-2.9-llama3-8b, and omost-phi-3-mini-128k, each with quantized variants. The README recommends omost-llama-3-8b-4bits for most users.
Can I edit a scene after the initial prompt in Omost?
Yes. Omost supports conversational editing: you can ask the model to change specific elements in the Canvas code, such as replacing one subject with another, then render the updated composition when you are satisfied.
How does Omost differ from a standard text-to-image prompt?
Instead of one prompt string, Omost generates structured Canvas code with separate global and local descriptions, spatial regions, and depth ordering. A custom diffusion renderer uses those regions to guide attention during image generation.
Where can I try Omost without installing it?
The Omost GitHub README links to an official HuggingFace space for browser-based demos. Local setup is also documented with conda, pip, and a gradio_app.py entry point.