HiDream-I1 is a powerful, open-source image generation model that delivers top-tier visual quality in just seconds. It handles a variety of styles — from photorealistic renders to stylized art — and follows written prompts with pinpoint accuracy. Designed for speed, flexibility, and creativity, it outperforms many popular tools on key quality benchmarks. Whether you’re creating stunning visuals for fun or for work, HiDream-I1 makes the process effortless and lightning-fast.
Recommended GPU Configuration
- GPU: 1× NVIDIA A100 / RTX A6000 / L40 / H100 (>= 40 GB VRAM is ideal)
- vCPU: 16+ cores
- RAM: 48 GB or more
- CUDA: 12.4 (mandatory for FlashAttention)
- Storage: 80+ GB SSD (model and dependency downloads)
Evaluation Metrics
DPG-Bench
| Model | Overall | Global | Entity | Attribute | Relation | Other |
|---|
| PixArt-alpha | 71.11 | 74.97 | 79.32 | 78.60 | 82.57 | 76.96 |
| SDXL | 74.65 | 83.27 | 82.43 | 80.91 | 86.76 | 80.41 |
| DALL-E 3 | 83.50 | 90.97 | 89.61 | 88.39 | 90.58 | 89.83 |
| Flux.1-dev | 83.79 | 85.80 | 86.79 | 89.98 | 90.04 | 89.90 |
| SD3-Medium | 84.08 | 87.90 | 91.01 | 88.83 | 80.70 | 88.68 |
| Janus-Pro-7B | 84.19 | 86.90 | 88.90 | 89.40 | 89.32 | 89.48 |
| CogView4-6B | 85.13 | 83.85 | 90.35 | 91.17 | 91.14 | 87.29 |
| HiDream-I1 | 85.89 | 76.44 | 90.22 | 89.48 | 93.74 | 91.83 |
GenEval
| Model | Overall | Single Obj. | Two Obj. | Counting | Colors | Position | Color attribution |
|---|
| SDXL | 0.55 | 0.98 | 0.74 | 0.39 | 0.85 | 0.15 | 0.23 |
| PixArt-alpha | 0.48 | 0.98 | 0.50 | 0.44 | 0.80 | 0.08 | 0.07 |
| Flux.1-dev | 0.66 | 0.98 | 0.79 | 0.73 | 0.77 | 0.22 | 0.45 |
| DALL-E 3 | 0.67 | 0.96 | 0.87 | 0.47 | 0.83 | 0.43 | 0.45 |
| CogView4-6B | 0.73 | 0.99 | 0.86 | 0.66 | 0.79 | 0.48 | 0.58 |
| SD3-Medium | 0.74 | 0.99 | 0.94 | 0.72 | 0.89 | 0.33 | 0.60 |
| Janus-Pro-7B | 0.80 | 0.99 | 0.89 | 0.59 | 0.90 | 0.79 | 0.66 |
| HiDream-I1 | 0.83 | 1.00 | 0.98 | 0.79 | 0.91 | 0.60 | 0.72 |
Step-by-Step Process to Install HiDream-I1: SOTA Image Generation with Text Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1 x H100 SXM GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy HiDream-I1 on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install HiDream-I1 on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Check the Available Python version and Install the new version
Run the following commands to check the available Python version.
If you check the version of the python, system has Python 3.8.1 available by default. To install a higher version of Python, you’ll need to use the deadsnakes PPA.
Run the following commands to add the deadsnakes PPA:
sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
Step 9: Install Python 3.11
Now, run the following command to install Python 3.11 or another desired version:
sudo apt install -y python3.11 python3.11-distutils python3.11-venv
Step 10: Update the Default Python3 Version
Now, run the following command to link the new Python version as the default python3:
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 2
sudo update-alternatives --config python3
Then, run the following command to verify that the new Python version is active:
python3 --version
Step 11: Install and Update Pip
Run the following command to install and update the pip:
python3 -m ensurepip --upgrade
python3 -m pip install --upgrade pip
Then, run the following command to check the version of pip:
pip --version
Step 12: Clone the Repository
Run the following command to clone the HiDream-I1 repository:
git clone https://github.com/HiDream-ai/HiDream-I1.git
cd HiDream-I1
Step 13: Install Dependencies
Run the following command to install the dependencies:
pip install -r requirements.txt
If you face FlashAttention issues, try manually installing it:
pip install flash-attn --no-build-isolation
Step 14: Login Using Your Hugging Face API Token
Use the huggingface_hub cli to login directly in the terminal.
Run the following command to login in huggingface-cli:
huggingface-cli login
Then, enter the token and press the Enter key. Ensure you press Enter after entering the token so the input will not be visible.
After entering the token, you will see the following output:
Login Successful.
The current active token is (your_token_name).
Check the screenshot below for reference.
How to Generate a Hugging Face Token
- Create an Account: Go to the Hugging Face website and sign up for an account if you don’t already have one.
- Access Settings: After logging in, click on your profile photo in the top right corner and select “Settings.”
- Navigate to Access Tokens: In the settings menu, find and click on the “Access Tokens” tab.
- Generate a New Token: Click the “New token” button, provide a name for your token, and choose a role (either
read or write).
- Generate and Copy Token: Click the “Generate a token” button. Your new token will appear; click “Show” to view it and copy it for use in your applications.
- Secure Your Token: Ensure you keep your token secure and do not expose it in public code repositories.
Step 15: Install Sentencepiece
Run the following command to install the sentencepiece:
pip install sentencepiece
Step 16: Run Inference
You can choose between full, dev, or fast model types:
# Full model (17B)
python inference.py --model_type full
# Or use dev/fast versions if limited by VRAM
# python inference.py --model_type dev
# python inference.py --model_type fast
Step 17: Install Gradio Web UI
Run the following command to install gradio web ui:
pip install gradio
Step 18: Launch Gradio Web UI
To run the interactive demo in your browser run the following command:
python3 gradio_demo.py
You should see output like:
Running on local URL: http://127.0.0.1:7860
Running on public URL: http://<your-vm-ip>:7860
If your VM has a public IP, access the Gradio interface at:
http://<your-vm-ip>:7860
Or locally with port forwarding:
ssh -L 7860:localhost:7860 root@<your-vm-ip>
Then open: http://localhost:7860
Conclusion
HiDream-I1 delivers a seamless image generation experience, combining visual quality, speed, and prompt precision in one open-source package. Whether you’re building projects for design, storytelling, or commercial use, this setup equips you with everything needed to generate high-quality visuals on your own terms. With just a few commands, your virtual machine becomes a creative powerhouse—ready to turn your ideas into stunning images in seconds.
