HunyuanVideo: Architecture, Capabilities, and Getting Started

What it is

HunyuanVideo is an open-source video foundation model developed by Tencent Hunyuan designed to generate high-quality video content from text inputs. With an architecture scaling beyond 13 billion parameters—the internal mathematical weights that govern how a model reasons and produces output—the project documentation identifies it as the largest open-source video generation model available at its release. The project is created to narrow the capability gap between closed-source commercial systems and open community research by providing publicly accessible model weights, inference code, and technical definitions.

At the technical center of HunyuanVideo is a unified Transformer architecture that supports image and video generation through a Full Attention mechanism. Processing high-resolution video frame by frame in standard pixel space requires unsustainable amounts of computational power. To make large-scale generation viable, HunyuanVideo operates inside a compact mathematical environment known as a latent space.

This compression is managed by a custom 3D Variational Autoencoder (3D VAE) built with CausalConv3D (causal three-dimensional convolutions). The autoencoder compresses data across three primary axes:

• Video time duration is compressed by a factor of 4.
• Spatial dimensions (height and width) are compressed by a factor of 8.
• Color channel information is compressed by a factor of 16.

By drastically lowering the number of visual tokens (discrete representations of pixel patches) that the model needs to process, the system can train and generate footage at original resolutions and frame rates without excessive memory overhead.

To handle prompt instructions and visual data simultaneously, HunyuanVideo utilizes a “dual-stream to single-stream” hybrid framework:

• Dual-stream stage: Text tokens from the prompt and visual tokens from the latent video space are first processed in separate, dedicated Transformer blocks. This ensures that text semantics and visual data establish their own internal modulation patterns independently without interference.
• Single-stream stage: The visual and textual tokens are concatenated into a combined sequence and fed into subsequent Transformer blocks. This allows the model to capture intricate interactions between visual structures and descriptive keywords through deep multimodal fusion.

Rather than relying on older combinations of CLIP and T5-XXL encoders, HunyuanVideo adopts a pre-trained Multimodal Large Language Model (MLLM) featuring a decoder-only structure. According to the project, this MLLM approach provides superior image detail recognition, stronger multi-step reasoning, and cleaner visual-text alignment. To compensate for the MLLM’s directional causal attention compared to bidirectional models, HunyuanVideo incorporates an extra bidirectional token refiner to supply balanced textual guidance to the diffusion pipeline.

What it can do

HunyuanVideo functions as a text-to-video generation engine capable of turning written language into continuous visual scenes. In addition to primary text-to-video generation, the project ecosystem encompasses several specialized capabilities and community extensions:

• Prompt Rewriting: User prompts vary significantly in descriptive quality and length. To adapt prompts into formats preferred by the diffusion model, the repository includes a fine-tuned version of the Hunyuan-Large model. This prompt rewrite tool features two operational modes:
  • Normal mode: Formulates clearer instructions to enhance the model’s comprehension of the user’s core intent.
  • Master mode: Enriches stylistic details such as camera motion, lighting effects, and shot composition to maximize visual aesthetic quality, though the documentation notes that this may occasionally overlook specific semantic details.
• Image Animation and Customization: Beyond text-to-video generation, the repository roadmap includes HunyuanVideo-I2V for image-to-video animation, HunyuanCustom for multimodal-guided personalized generation, and HunyuanVideo-Avatar for audio-driven human animations.
• Efficient Inference and Formats: The project supports parallel sequence inference across multiple GPUs using the xDiT library, official integration into Hugging Face Diffusers, interactive web interfaces via Gradio, and quantized 8-bit floating point (FP8) model weights to reduce GPU memory requirements.
• Community Integrations: The project documents various community tools, including ComfyUI wrappers, native ComfyUI nodes, distilled acceleration pipelines such as FastVideo, GGUF quantization formats, keyframe control LoRA modules, and caching utilities like TeaCache and NaviCache.

According to the project README, in comparative evaluations conducted across 1,533 text prompts with over 60 professional evaluators, HunyuanVideo ranked first overall when evaluated against several closed-source commercial baselines. The project README reports that HunyuanVideo achieved an overall evaluation score of 41.3%, with 61.8% text alignment, 66.5% motion quality, and 95.7% visual quality, outperforming baselines such as Runway Gen-3 alpha, Luma 1.6, and three leading Chinese commercial models (CNTopA, CNTopB, and CNTopC). The project notes that this evaluation was carried out using its high-quality model configuration rather than the released fast version.

How to get started

Deploying HunyuanVideo locally requires a Linux operating system, an NVIDIA GPU with CUDA support, and an installed Python environment managed via Conda.

First, clone the repository using Git:

git clone https://github.com/Tencent-Hunyuan/HunyuanVideo
cd HunyuanVideo

Next, configure the Conda environment and install PyTorch alongside the required Python libraries. The project suggests CUDA 12.6 or CUDA 11.8:

# 1. Create conda environment
conda create -n HunyuanVideo python==3.10.9

# 2. Activate the environment
conda activate HunyuanVideo

# 3. Install PyTorch
# For CUDA 11.8
pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu118
# For CUDA 12.6
pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu126

# 4. Install pip dependencies
python -m pip install -r requirements.txt

# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
python -m pip install ninja
python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.8.3

# 6. Install xDiT for parallel inference (It is recommended to use torch 2.7.1 and flash-attn 2.8.3)
python -m pip install xfuser==0.4.0

For systems that encounter floating-point exceptions or core dumps during setup, the repository also provides pre-built Docker images:

# For CUDA 12.4 (updated to avoid float point exception)
docker pull hunyuanvideo/hunyuanvideo:cuda_12
docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo:cuda_12

# For CUDA 11.8
docker pull hunyuanvideo/hunyuanvideo:cuda_11
docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged

Once the environment is prepared, users can download the pre-trained model checkpoints from Hugging Face and trigger inference scripts via the command-line interface, launch a local Gradio browser demo, or run multi-GPU setups using xDiT.

Pricing and requirements

Pricing and plans are subject to the official page at https://aivideo.hunyuan.tencent.com. The repository source code, inference scripts, and pre-trained model weights are distributed openly online, though the repository metadata lists the specific open-source license field as NOASSERTION.

Running the full-scale foundation model requires substantial computing resources:

• Operating System: Tested on Linux.
• Hardware Architecture: Requires an NVIDIA GPU with CUDA drivers. The system was validated by the developers on a single 80GB GPU.
• Memory Demands (Batch Size = 1):
  • 720p Resolution (720x1280 pixels, 129 frames): Requires a minimum GPU peak memory of 60GB.
  • 544p Resolution (544x960 pixels, 129 frames): Requires a minimum GPU peak memory of 45GB.
  • Recommended Setup: A GPU with 80GB of memory is recommended for superior generation performance.
• Optimization Paths: To accommodate varying hardware constraints, the project offers FP8 precision weights that lower memory requirements, as well as multi-GPU sequence parallelism via xDiT.

Who it is for

HunyuanVideo is intended for several distinct groups working with AI video generation:

• AI Researchers and Machine Learning Engineers: Researchers studying multimodal foundation models, full-attention Transformer backbones, and spatial-temporal autoencoding benefit from inspectable weights and inference pipelines in a 13-billion-parameter system.
• High-Performance Infrastructure Operators: Developers, creative studios, and engineering teams that maintain high-memory enterprise GPUs (such as 80GB accelerators or multi-GPU servers) and need a self-hosted video generation solution.
• Tool Builders and Open-Source Developers: Integrators who design modular generative workflows using systems such as Hugging Face Diffusers or ComfyUI, tailoring model weights to automated production tasks.
• Technical Creators and Animators: Experienced digital creators who want to experiment with advanced prompt rewrite mechanisms, image-to-video extensions, and high-fidelity video generation.

Alternatives

Wan2.1 is an open-source video generation model repository available on GitHub.

LTX-Video is an open-source video generation project developed by Lightricks.

Sources

• https://github.com/Tencent-Hunyuan/HunyuanVideo
• https://github.com/Wan-Video/Wan2.1
• https://github.com/Lightricks/LTX-Video