OmniGen - AI Image Models Tool

Overview

OmniGen is an open-source unified image-generation diffusion model that accepts arbitrarily multi-modal prompts (text plus one or more images) and produces a wide range of outputs — from text‑to‑image generation to instruction-guided edits and identity/object-preserving synthesis. The project emphasizes simplicity: rather than relying on external adapters or control modules (e.g., ControlNet, IP‑Adapter, Reference‑Net), OmniGen is trained and designed to interpret mixed inputs and control instructions directly, enabling pipelines like subject-driven generation, multi-image referring expressions, and editing without extra preprocessing steps. ([github.com](https://github.com/VectorSpaceLab/OmniGen)) OmniGen was trained using a large unified dataset (X2I, the authors report ~0.1 billion image examples converted into a single task format) and ships code, inference notebooks, and prebuilt model weights (Shitao/OmniGen-v1). The project provides an inference pipeline compatible with the Diffusers style API, a Hugging Face demo, and a Replicate-hosted demo — and supports local fine‑tuning (including LoRA examples) so researchers and practitioners can extend the model to new subject-driven or editing tasks. The codebase, examples, and guidance for resource‑aware inference (offload options, max input image size) are included in the repository. ([vectorspacelab.github.io](https://vectorspacelab.github.io/OmniGen/?utm_source=openai))

GitHub Statistics

• Stars: 4,298
• Forks: 368
• Contributors: 15
• License: MIT
• Primary Language: Jupyter Notebook
• Last Updated: 2025-12-04T15:59:12Z

The GitHub repository shows strong community interest (≈4.3k stars and 368 forks) and is MIT‑licensed. The codebase contains notebooks, inference and fine‑tuning scripts, and an examples folder; repository metadata lists ~157 commits, ~138 open issues, ~7 open pull requests, and 15 contributors. Development continued into 2025 (project page and commit history reflect activity through 2025). The project has been integrated into downstream ecosystems (Diffusers and third‑party UIs), which indicates reasonable maintenance and adoption beyond the core repo. ([github.com](https://github.com/VectorSpaceLab/OmniGen))

Installation

Install via pip:

git clone https://github.com/VectorSpaceLab/OmniGen.git

cd OmniGen

pip install -e .

# (optional) create and activate a conda env

conda create -n omnigen python=3.10.13

conda activate omnigen

# install PyTorch for your CUDA version (example for CUDA 11.8)

pip install torch==2.3.1+cu118 torchvision --extra-index-url https://download.pytorch.org/whl/cu118

# (optional) for local Gradio demo

pip install gradio spaces

python app.py

Key Features

• Unified multi‑modal generation: single model handles text, single/multiple images, and mixed prompts. ([github.com](https://github.com/VectorSpaceLab/OmniGen))
• Text‑to‑image: high‑resolution 1024×1024 default text→image generation via OmniGenPipeline. ([github.com](https://github.com/VectorSpaceLab/OmniGen))
• Identity/object preserving: extracts and reuses people or objects from inputs for subject‑driven generation. ([huggingface.co](https://huggingface.co/docs/diffusers/using-diffusers/omnigen?utm_source=openai))
• Instruction‑guided image editing: modify specific regions or attributes using natural language prompts. ([github.com](https://github.com/VectorSpaceLab/OmniGen))
• Fine‑tuning & LoRA support: training scripts and a LoRA example enable subject‑driven customization. ([github.com](https://github.com/VectorSpaceLab/OmniGen))

Community

OmniGen has broad community signals: ~4.3k GitHub stars, 368 forks, and 15 contributors; an active issues list (~138) indicates usage and feedback. The model is available as Hugging Face/Diffusers artifacts and a hosted demo (Hugging Face and Replicate), and third‑party integrations (ComfyUI community projects) exist for easier local use. The authors published an arXiv technical report and open‑sourced the X2I dataset; a successor project (OmniGen2) and tooling updates (Diffusers integration, inference optimizations) followed in 2025, showing an evolving ecosystem. Users report demo/Replicate run times and per‑run costs on hosted services; the codebase provides offload and input‑size knobs to manage VRAM and inference time. For more details and hands‑on examples, check the repository, Diffusers docs, and the Replicate demo. ([github.com](https://github.com/VectorSpaceLab/OmniGen))

Key Information

• Category: Image Models
• Type: AI Image Models Tool