SkyReels V1 is revolutionizing the world of open-source text-to-video models with its cutting-edge capabilities in human-centric video generation. Built on the foundation of HunyuanVideo and fine-tuned on over 10 million high-quality film and television clips, SkyReels V1 stands out with its state-of-the-art performance, rivaling even proprietary models like Kling and Hailuo. What sets it apart is its ability to capture nuanced facial expressions—33 distinct types with over 400 movement combinations—ensuring lifelike emotional representation. Additionally, the model excels in cinematic aesthetics, replicating Hollywood-level lighting, composition, and character positioning. Whether you’re an AI enthusiast, a content creator, or a developer exploring text-to-video generation, SkyReels V1 opens up a world of creative possibilities.
In this guide, we have simplified the process of installing and running SkyReels in a step-by-step approach, so anyone can start using it in no time.
Prerequisites
The minimum system requirements for this use case are:
- GPUs: RTX 4090 or RTX A6000 (for smooth execution).
- Disk Space: 150 GB
- RAM: At least 16 GB.
- Jupyter Notebook installed.
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 & run SkyReels-V1 Hunyuan
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 the RTX 4090 GPU; however, you can choose any GPU of your choice based on your needs.
- 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 VMs in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX 4090 GPU node with 12 vCPUs/96GB RAM/200 GB 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 would be to choose an image for the VM. This particular model can be easily installed using a terminal with Nvidia Cuda image, but for seamlessly seeing our test results in the local folder, we will install it in the terminal on Jupyter Image.
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 and click on Connect with SSH. This will open a new tab with a Jupyter Notebook session in which we can run our model.
Step 7: Setting up Python Notebook
Start by creating a .ipynb notebook by clicking on Python 3 (ipykernel).
Next, If you want to check the GPU details, run the following command in the Jupyter Notebook cell:
!nvidia-smi
Output:
Step 8: Set up the project environment with dependencies
- Move inside the project directory and create a virtual environment with Anaconda.
conda create -n skyreels python=3.11 -y
Output:
2. Activate the environment, and clone the official SkyReels-V1 repository.
conda activate
git clone https://github.com/SkyworkAI/SkyReels-V1.git
3. Move inside the project directory and install dependencies as mentioned in requirements.txt.
cd SkyReels-V1
pip install -r requirements.txt
Output:
Step 9: Run and test the model
To run model inference run the whole snippet at once after replacing the test prompt with your own prompt:
SkyReelsModel="Skywork/SkyReels-V1-Hunyuan-T2V"
python3 video_generate.py \
--model_id ${SkyReelsModel} \
--task_type t2v \
--guidance_scale 6.0 \
--height 544 \
--width 960 \
--num_frames 97 \
--prompt "FPS-24, A cat wearing sunglasses and working as a lifeguard at a pool" \
--embedded_guidance_scale 1.0
If you’re not able to run the above command snippet in the terminal, run the following one by one:
export SkyReelsModel="Skywork/SkyReels-V1-Hunyuan-T2V"
python3 video_generate.py \
--model_id ${SkyReelsModel} \
--task_type t2v \
--guidance_scale 6.0 \
--height 544 \
--width 960 \
--num_frames 97 \
--prompt "FPS-24, A cat wearing sunglasses and working as a lifeguard at a pool" \
--embedded_guidance_scale 1.0
Output:
Below is the link containing the output video generated by the model on the given prompt:
Prompt: A cat wearing sunglasses and working as a lifeguard at a pool
Result: https://drive.google.com/drive/folders/1suBQAoUg3CGxkHa_jJdrbkUSMxYilyQl?usp=sharing
Conclusion
SkyReels V1 represents a breakthrough in open-source text-to-video models, combining state-of-the-art facial animation, cinematic aesthetics, and a multi-stage image-to-video pretraining pipeline for realistic human-centric video generation. Its ability to process vast datasets with precision makes it a powerful tool for creators and developers alike. By deploying SkyReels Hunyuan on NodeShift’s decentralized cloud stack, users can harness its full potential with enhanced scalability, security, and reliability, ensuring seamless video generation without the limitations of traditional cloud infrastructures.
