Have you ever wanted to create and explore vast, consistent 3D worlds from a single image? While previous models like HunyuanWorld 1.0 have made strides in explorable 3D world generation, they often struggle with occluded views and limited exploration ranges. This is where HunyuanWorld-Voyager comes in, a groundbreaking video diffusion framework that shatters these limitations by generating world-consistent 3D point-cloud sequences. Voyager addresses these challenges head-on, offering a world-consistent video diffusion framework capable of generating coherent 3D point-cloud sequences from a single image along a user-defined camera path. By integrating world-consistent video diffusion, long-range world exploration, and a scalable data engine, Voyager ensures end-to-end scene generation and reconstruction without relying on traditional 3D reconstruction pipelines. Its innovative architecture jointly generates aligned RGB and depth sequences, leverages an efficient world cache with point culling for iterative exploration, and automatically predicts camera poses and metric depths, delivering unmatched visual fidelity and geometric accuracy.
This guide will walk you through the installation of HunyuanWorld-Voyager, detailing each step of the complete setup process.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x H100 or 1x H200
- Storage: 200 GB (preferable)
- VRAM: at least 85 GB
- Anaconda installed
Step-by-step process to install and run HunyuanWorld-Voyager
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.
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 HunyuanWorld-Voyager, we need a more customized environment with full CUDA12.4 development capabilities as mentioned in their requirements. That’s why, 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-ubuntu22.04
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:12.1.1-devel-ubuntu22.04
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 voyager python==3.11.9 -y && conda activate voyager
Output:
2. Clone the official repository of the project.
git clone https://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager && cd HunyuanWorld-Voyager
Output:
3. Install required dependencies.
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
python -m pip install -r requirements.txt
python -m pip install transformers==4.39.3
python -m pip install flash-attn
python -m pip install xfuser==0.4.2
apt-get update
apt-get install -y libgl1 libglib2.0-0
Output:
Step 8: Download and Run the model
1. Come outside of the repo directory, and download model checkpoints with the following command.
cd ..
huggingface-cli download tencent/HunyuanWorld-Voyager --local-dir ./ckpts
2. Run the model for inference using the given example image inputs.
python3 sample_image2video.py \
--model HYVideo-T/2 \
--input-path "examples/case1" \
--prompt "An old-fashioned European village with thatched roofs on the houses." \
--i2v-stability \
--infer-steps 50 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--use-cpu-offload \
--save-path ./results
If you want to run with your custom image and/or prompt, then run the following command before running the above command.
(replace your_input_image with your input image path)
cd data_engine
python3 create_input.py --image_path "your_input_image" --render_output_dir "examples/case/" --type "forward"
Output:
Here’s the given example input image we used in our case:
Here’s the final generated video:
Conclusion
HunyuanWorld-Voyager not only transforms single images into immersive, world-consistent 3D video experiences but also streamlines the entire process with efficiency and scalability. By combining advanced video diffusion, long-range world exploration, and automated scene reconstruction, Voyager ensures both visual fidelity and geometric accuracy across extended scenes. NodeShift Cloud plays a pivotal role in this workflow, providing a robust, cloud-based intuitive platform and on-prem local private enterprise environment that simplifies deployment, accelerates computation, and enables seamless handling of large-scale models and datasets. Together, Voyager’s cutting-edge technology and NodeShift’s scalable infrastructure empower creators and researchers to explore, generate, and expand 3D worlds like never before, making sophisticated 3D video generation accessible and efficient for all teams, big or small.
