When it comes to text-to-image generation, most models either compromise on resolution, speed, or semantic accuracy, but HunyuanImage 2.1 changes the game. This latest open-source model from Tencent pushes the boundaries of AI creativity by generating ultra-high-definition 2K (2048×2048) images while maintaining remarkable efficiency. At its core, it combines a high-compression VAE (32× spatial compression) with a dual-stream diffusion transformer boasting 17 billion parameters, ensuring cinematic-quality visuals at a fraction of the computational cost. With a two-stage architecture, first creating highly aligned base images using dual text encoders (multimodal and multilingual), then refining them with a dedicated enhancement model, HunyuanImage 2.1 excels at producing lifelike details, sharp compositions, and seamless alignment between text and visuals. Features like prompt rewriting with PromptEnhancer, reinforcement learning from human feedback (RLHF), and meanflow-based efficient inference make it powerful, and practical for real-world creative workflows.
Let’s dive into the quick and simple step-by-step installation and inference process for this model.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x H100 or 1x H200
- Storage: 250 GB (preferable)
- VRAM: at least 64 GB
- Anaconda installed
Step-by-step process to install and run HunyuanImage-2.1
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 H200 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 H200 140GB GPU node with 192 vCPUs/252GB 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.
In our previous blogs, we used pre-built images from the Templates tab when creating a Virtual Machine. However, for running a CUDA dependent model like HunyuanImage-2.1, we need a latest CUDA version like CUDA12.4. Therefore, in this case, we switched to the Custom Image tab and selected a specific Docker image that meets all runtime and compatibility requirements.
We chose the following image:
nvidia/cuda:12.4.0-devel-ubuntu24.04
This image is essential because it includes:
- Full CUDA toolkit (including
nvcc)
- Proper support for building and running GPU-based applications
- Compatibility with CUDA 12.4.0 required by model operations
Launch Mode
We selected:
Interactive shell server
This gives us SSH access and full control over terminal operations — perfect for installing dependencies, running benchmarks, and launching models.
Docker Repository Authentication
We left all fields empty here.
Since the Docker image is publicly available on Docker Hub, no login credentials are required.
Identification
nvidia/cuda:12.4.0-devel-ubuntu24.04
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 hunyuan python=3.11 -y && conda activate hunyuan
Output:
2. Clone the official repository of the project.
git clone https://github.com/Tencent-Hunyuan/HunyuanImage-2.1.git && cd HunyuanImage-2.1
Output:
3. Install required dependencies.
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
apt-get update
apt-get install -y libgl1 libglib2.0-0
pip install -U "huggingface_hub[cli]"
pip install modelscope
Output:
Step 8: Download and Run the model
1. Download model checkpoints with the following command.
hf download tencent/HunyuanImage-2.1 --local-dir ./ckpts
hf download Qwen/Qwen2.5-VL-7B-Instruct --local-dir ./ckpts/text_encoder/llm
hf download google/byt5-small --local-dir ./ckpts/text_encoder/byt5-small
modelscope download --model AI-ModelScope/Glyph-SDXL-v2 --local_dir ./ckpts/text_encoder/Glyph-SDXL-v2
2. Install and run jupyter notebook.
conda install -c conda-forge --override-channels notebook -y
conda install -c conda-forge --override-channels ipywidgets -y
jupyter notebook --allow-root
3. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the jupyter notebook 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 8888:localhost:8888 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Output:
After this copy the URL you received in your remote server:
And paste this on your local browser to access the Jupyter Notebook session.
4. Run the model for inference by executing the following code in the Jupyter notebook cell with your desired prompt.
import torch
from hyimage.diffusion.pipelines.hunyuanimage_pipeline import HunyuanImagePipeline
# Supported model_name: hunyuanimage-v2.1, hunyuanimage-v2.1-distilled
model_name = "hunyuanimage-v2.1"
pipe = HunyuanImagePipeline.from_pretrained(model_name=model_name, torch_dtype='bf16')
pipe = pipe.to("cuda")
prompt = "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, wearing a red knitted scarf and a red beret with the word “Tencent” on it, holding a paintbrush with a focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
image = pipe(
prompt=prompt,
# Examples of supported resolutions and aspect ratios for HunyuanImage-2.1:
# 16:9 -> width=2560, height=1536
# 4:3 -> width=2304, height=1792
# 1:1 -> width=2048, height=2048
# 3:4 -> width=1792, height=2304
# 9:16 -> width=1536, height=2560
# Please use one of the above width/height pairs for best results.
width=2048,
height=2048,
use_reprompt=True, # Enable prompt enhancement
use_refiner=True, # Enable refiner model
# For the distilled model, use 8 steps for faster inference.
# For the non-distilled model, use 50 steps for better quality
num_inference_steps=8 if "distilled" in model_name else 50,
guidance_scale=3.5,
shift=5,
seed=649151,
)
image.save(f"generated_image.png")
Output:
Here’s the final generated image:
Conclusion
HunyuanImage 2.1 is an impressive, open-source model that overcomes common limitations in text-to-image generation by offering high-resolution 2K output, exceptional efficiency, and robust semantic alignment through a two-stage architecture and advanced features like PromptEnhancer and RLHF. To make this powerful model truly accessible for widespread use, a robust and scalable infrastructure is necessary. NodeShift Cloud provides the ideal environment for this, offering a decentralized platform with cost-effective, on-demand GPU resources. This synergy allows creators and developers to leverage HunyuanImage 2.1’s cutting-edge capabilities without the significant upfront investment and maintenance costs of dedicated hardware, democratizing access to professional-grade AI tools and enabling seamless, scalable creative workflows for everyone.
