What if one model could understand images like a seasoned analyst, generate stunning visuals from plain text, edit pictures based on your instructions, and even combine people, objects, and scenes into coherent new images, all without switching tools or pipelines? OmniGen2 is the one we’re talking about, the latest open-source powerhouse redefining what’s possible in multimodal AI. Building on the solid foundation of Qwen-VL-2.5, OmniGen2 is a unified any-to-any model that introduces a dual-decoder design, one pathway each for text and image outputs. This architecture leverages unshared parameters and a decoupled image tokenizer, enhancing both efficiency and specialization. If you’re developing a visual reasoning agent, crafting high-quality text-to-image applications, or building personalized image editors, OmniGen2 delivers state-of-the-art performance across four primary domains: visual understanding, instruction-based image editing, text-to-image generation, and in-context visual synthesis. And with training code and datasets on the way, it’s not just a model, it’s a full-stack solution for generative AI innovation.
In this guide, we’ll walk you through how to install OmniGen2 and unleash its full potential, step-by-step.
Prerequisites
The minimum system requirements for this use case are:
- GPUs: 1x RTX 4090 or 1x RTX A6000
- Disk Space: 50 GB
- RAM: At least 18 GB.
- Anaconda set up
Note: The prerequisites for this are highly variable across use cases. A high-end configuration could be used for a large-scale deployment.
Step-by-step process to install and run OmniGen2
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n omnigen python=3.11 && conda activate omnigen
Output:
2. Clone the official repository of VectorSpaceLab/OmniGen2 and move inside the project directory.
git clone https://github.com/VectorSpaceLab/OmniGen2.git && cd OmniGen2
Output:
3. Install PyTorch dependencies.
pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124
Output:
4. Install other required project dependencies.
pip install -r requirements.txt
Output:
5. Install flash_attn.
pip install flash-attn==2.7.4.post1 --no-build-isolation
Output:
6. Install Gradio.
pip install gradio
Output:
Step 8: Download and Run the Model
- Launch the Gradio app with
--share argument to get the public live URL of the app.
python app_chat.py --share
Output:
Once app is up and running, you’ll see a public URL where you can access the app.
2. If you do not want to create a public URL and instead want to access the app with local URL, you can access it by visiting http://127.0.0.1:7860.
However, if you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the local Gradio URL session on your local browser.
Run the following command in your local terminal after replacing:
<YOUR_SERVER_PORT> with the PORT allotted to your remote server (For the NodeShift server – you can find it in the deployed GPU details on the dashboard).
<PATH_TO_SSH_KEY> with the path to the location where your SSH key is stored.
<YOUR_SERVER_IP> with the IP address of your remote server.
ssh -L 7860:localhost:7860 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Once you’re connected, you’ll be able to access the app on local browser at this URL: http://127.0.0.1:7860
3. Finally, we’ll test one of the many features of the model, i.e., Image Editing.
Prompt: Replace the sword with a hammer.
Result:
Conclusion
OmniGen2 is a multimodal model, a robust, unified system built to handle complex visual understanding, high-quality text-to-image generation, precise instruction-based image editing, and powerful in-context composition, all with unmatched flexibility. Throughout this guide, we explored how to set up and tap into its full potential. But what truly streamlines this experience is deploying it on NodeShift cloud, which eliminates infrastructure headaches and lets you scale your experiments with ease. With pre-configured environments, GPU acceleration, and one-click deployment, NodeShift empowers developers and researchers to focus on what matters most, building the next generation of intelligent, creative applications using OmniGen2.
