OmniGen2

🔥 News

• 2025-07-05: Training datasets X2I2 are available.
• 2025-07-03: OmniGen2 now supports TeaCache and TaylorSeer for faster inference, see Usage Tips for details. Thanks @legitnull for great TeaCache-PR and TaylorSeer-PR.
• 2025-07-01: OmniGen2 is supported by ComfyUI official, thanks !!
• 2025-06-30: Training code is available, see fine-tuning for details.
• 2025-06-28: We release OmniContext benchmark. The evaluation codes are in omnicontext.
• 2025-06-24: Technical Report is available.
• 2025-06-23: We’ve updated our code and HF model—OmniGen2 now runs without flash-attn. Users can still install it for optimal performance.
• 2025-06-20: Updated resource requirements, adding CPU offload support for devices with limited VRAM.
• 2025-06-16: Gradio and Jupyter is available. Online Gradio Demo: Demo1; Chat-Demo1; see more demo links in gradio section
• 2025-06-16: We release OmniGen2, a multimodal generation model, model weights can be accessed in huggingface and modelscope.

Introduction

OmniGen2 is a powerful and efficient unified multimodal model. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. OmniGen2 has competitive performance across four primary capabilities:

• Visual Understanding: Inherits the robust ability to interpret and analyze image content from its Qwen-VL-2.5 foundation.
• Text-to-Image Generation: Creates high-fidelity and aesthetically pleasing images from textual prompts.
• Instruction-guided Image Editing: Executes complex, instruction-based image modifications with high precision, achieving state-of-the-art performance among open-source models.
• In-context Generation: A versatile capability to process and flexibly combine diverse inputs—including humans, reference objects, and scenes—to produce novel and coherent visual outputs.

As an open-source project, OmniGen2 provides a powerful yet resource-efficient foundation for researchers and developers exploring the frontiers of controllable and personalized generative AI.

We will release the training code, dataset, and data construction pipeline soon. Stay tuned!

📌 TODO

• [x] Technical report.
• [x] Support CPU offload and improve inference efficiency.
• [x] In-context generation benchmark: OmniContext.
• [ ] Integration of diffusers.
• [x] Training datasets.
• [ ] Training data construction pipeline.
• [ ] ComfyUI Demo (commuity support will be greatly appreciated!).

🚀 Quick Start

🛠️ Environment Setup

✅ Recommended Setup

# 1. Clone the repo
git clone git@github.com:VectorSpaceLab/OmniGen2.git
cd OmniGen2

# 2. (Optional) Create a clean Python environment
conda create -n omnigen2 python=3.11
conda activate omnigen2

# 3. Install dependencies
# 3.1 Install PyTorch (choose correct CUDA version)
pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124

# 3.2 Install other required packages
pip install -r requirements.txt

# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
# OmniGen2 runs even without flash-attn, though we recommend install it for best performance.
pip install flash-attn==2.7.4.post1 --no-build-isolation

🌏 For users in Mainland China

# Install PyTorch from a domestic mirror
pip install torch==2.6.0 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu124

# Install other dependencies from Tsinghua mirror
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
# OmniGen2 runs even without flash-attn, though we recommend install it for best performance.
pip install flash-attn==2.7.4.post1 --no-build-isolation -i https://pypi.tuna.tsinghua.edu.cn/simple

🧪 Run Examples

# Visual Understanding
bash example_understanding.sh

# Text-to-image generation
bash example_t2i.sh

# Instruction-guided image editing
bash example_edit.sh

# In-context generation
bash example_in_context_generation.sh

🌐 Gradio Demo

• Online Demo: HF Spaces. Beyond Hugging Face Spaces, we are temporarily allocating additional GPU resources to ensure smooth access to the online demos. If you notice a long queue for a particular link, please try other links:

  Demo1, Demo2, Demo3, Demo4

  Chat-Demo1, Chat-Demo2, Chat-Demo3, [Chat-Demo4](