HunyuanVideo: A Systematic Framework For Large Video Generation Model Training
This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring HunyuanVideo. You can find more visualizations on our
project page.
News!!
- Jan 13, 2025: 📈 We release the Penguin Video Benchmark.
- Dec 18, 2024: 🏃♂️ We release the FP8 model weights of HunyuanVideo to save more GPU memory.
- Dec 17, 2024: 🤗 HunyuanVideo has been integrated into Diffusers.
- Dec 7, 2024: 🚀 We release the parallel inference code for HunyuanVideo powered by xDiT.
- Dec 3, 2024: 👋 We release the inference code and model weights of HunyuanVideo. Download.
Open-source Plan
- HunyuanVideo (Text-to-Video Model)
- HunyuanVideo (Image-to-Video Model)
Contents
Abstract
We present HunyuanVideo, a novel open-source video foundation model that exhibits performance in video generation that is comparable to, if not superior to, leading closed-source models. In order to train HunyuanVideo model, we adopt several key technologies for model learning, including data curation, image-video joint model training, and an efficient infrastructure designed to facilitate large-scale model training and inference. Additionally, through an effective strategy for scaling model architecture and dataset, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models.
We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion diversity, text-video alignment, and generation stability. According to professional human evaluation results, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and 3 top-performing Chinese video generative models. By releasing the code and weights of the foundation model and its applications, we aim to bridge the gap between closed-source and open-source video foundation models. This initiative will empower everyone in the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem.
HunyuanVideo Overall Architecture
HunyuanVideo is trained on a spatial-temporally
compressed latent space, which is compressed through a Causal 3D VAE. Text prompts are encoded
using a large language model, and used as the conditions. Taking Gaussian noise and the conditions as
input, our generative model produces an output latent, which is then decoded to images or videos through
the 3D VAE decoder.
HunyuanVideo Key Features
Unified Image and Video Generative Architecture
HunyuanVideo introduces the Transformer design and employs a Full Attention mechanism for unified image and video generation.
Specifically, we use a "Dual-stream to Single-stream" hybrid model design for video generation. In the dual-stream phase, video and text
tokens are processed independently through multiple Transformer blocks, enabling each modality to learn its own appropriate modulation mechanisms without interference. In the single-stream phase, we concatenate the video and text
tokens and feed them into subsequent Transformer blocks for effective multimodal information fusion.
This design captures complex interactions between visual and semantic information, enhancing
overall model performance.
MLLM Text Encoder
Some previous text-to-video models typically use pre-trained CLIP and T5-XXL as text encoders where CLIP uses Transformer Encoder and T5 uses an Encoder-Decoder structure. In contrast, we utilize a pre-trained Multimodal Large Language Model (MLLM) with a Decoder-Only structure as our text encoder, which has the following advantages: (i) Compared with T5, MLLM after visual instruction finetuning has better image-text alignment in the feature space, which alleviates the difficulty of the instruction following in diffusion models; (ii)
Compared with CLIP, MLLM has demonstrated superior ability in image detail description
and complex reasoning; (iii) MLLM can play as a zero-shot learner by following system instructions prepended to user prompts, helping text features pay more attention to key information. In addition, MLLM is based on causal attention while T5-XXL utilizes bidirectional attention that produces better text guidance for diffusion models. Therefore, we introduce an extra bidirectional token refiner to enhance text features.
3D VAE
HunyuanVideo trains a 3D VAE with CausalConv3D to compress pixel-space videos and images into a compact latent space. We set the compression ratios of video length, space, and channel to 4, 8, and 16 respectively. This can significantly reduce the number of tokens for the subsequent diffusion transformer model, allowing us to train videos at the original resolution and frame rate.
Prompt Rewrite
To address the variability in linguistic style and length of user-provided prompts, we fine-tune the
Hunyuan-Large model as our prompt rewrite model to adapt the original user prompt to model-preferred prompt.
We provide two rewrite modes: Normal mode and Master mode, which can be called using different prompts. The prompts are shown
here. The Normal mode is designed to enhance the video generation model's comprehension of user intent, facilitating a more accurate interpretation of the instructions provided. The Master mode enhances the description of aspects such as composition, lighting, and camera movement, which leans towards generating videos with a higher visual quality. However, this emphasis may occasionally result in the loss of some semantic details.
The Prompt Rewrite Model can be directly deployed and inferred using the
Hunyuan-Large original code. We release the weights of the Prompt Rewrite Model
here.
Comparisons
To evaluate the performance of HunyuanVideo, we selected five strong baselines from closed-source video generation models. In total, we utilized 1,533 text prompts, generating an equal number of video samples with HunyuanVideo in a single run. For a fair comparison, we conducted inference only once, avoiding any cherry-picking of results. When comparing with the baseline methods, we maintained the default settings for all selected models, ensuring consistent video resolution. Videos were assessed based on three criteria: Text Alignment, Motion Quality, and Visual Quality. More than 60 professional evaluators performed the evaluation. Notably, HunyuanVideo demonstrated the best overall performance, particularly excelling in motion quality. Please note that the evaluation is based on Hunyuan Video's high-quality version. This is different from the currently released fast version.
| Model | Open Source | Duration | Text Alignment | Motion Quality | Visual Quality | Overall | Ranking |
|---|
| HunyuanVideo (Ours) | ✔ | 5s | 61.8% | 66.5% | 95.7% | 41.3% | 1 |
| CNTopA (API) | ✘ | 5s | 62.6% | 61.7% | 95.6% | 37.7% | 2 |
| CNTopB (Web) | ✘ | 5s | 60.1% | 62.9% | 97.7% | 37.5% | 3 |
| GEN-3 alpha (Web) | ✘ | 6s | 47.7% | 54.7% | 97.5% | 27.4% | 4 |
| Luma1.6 (API) | ✘ | 5s | 57.6% | 44.2% | 94.1% | 24.8% | 5 |
| CNTopC (Web) | ✘ | 5s | 48.4% | 47.2% | 96.3% | 24.6% | 6 |
Requirements
The following table shows the requirements for running HunyuanVideo model (batch size = 1) to generate videos:
| Model | Setting
(height/width/frame) | GPU Peak Memory |
|---|
| HunyuanVideo | 720px1280px129f | 60GB |
| HunyuanVideo | 544px960px129f | 45GB |
- An NVIDIA GPU with CUDA support is required.
- The model is tested on a single 80G GPU.
- Minimum: The minimum GPU memory required is 60GB for 720px1280px129f and 45G for 544px960px129f.
- Recommended: We recommend using a GPU with 80GB of memory for better generation quality.
- Tested operating system: Linux
Dependencies and Installation
Begin by cloning the repository:
1git clone https://github.com/tencent/HunyuanVideo
2cd HunyuanVideo
Installation Guide for Linux
We recommend CUDA versions 12.4 or 11.8 for the manual installation.
Conda's installation instructions are available
here.
1# 1. Create conda environment
2conda create -n HunyuanVideo python==3.10.9
3
4# 2. Activate the environment
5conda activate HunyuanVideo
6
7# 3. Install PyTorch and other dependencies using conda
8# For CUDA 11.8
9conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=11.8 -c pytorch -c nvidia
10# For CUDA 12.4
11conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
12
13# 4. Install pip dependencies
14python -m pip install -r requirements.txt
15
16# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
17python -m pip install ninja
18python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
19
20# 6. Install xDiT for parallel inference (It is recommended to use torch 2.4.0 and flash-attn 2.6.3)
21python -m pip install xfuser==0.4.0
In case of running into float point exception(core dump) on the specific GPU type, you may try the following solutions:
1# Option 1: Making sure you have installed CUDA 12.4, CUBLAS>=12.4.5.8, and CUDNN>=9.00 (or simply using our CUDA 12 docker image).
2pip install nvidia-cublas-cu12==12.4.5.8
3export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
4
5# Option 2: Forcing to explictly use the CUDA 11.8 compiled version of Pytorch and all the other packages
6pip uninstall -r requirements.txt # uninstall all packages
7pip uninstall -y xfuser
8pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu118
9pip install -r requirements.txt
10pip install ninja
11pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
12pip install xfuser==0.4.0
Additionally, HunyuanVideo also provides a pre-built Docker image. Use the following command to pull and run the docker image.
1# For CUDA 12.4 (updated to avoid float point exception)
2docker pull hunyuanvideo/hunyuanvideo:cuda_12
3docker 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
4
5# For CUDA 11.8
6docker pull hunyuanvideo/hunyuanvideo:cuda_11
7docker 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_11
Download Pretrained Models
The details of download pretrained models are shown
here.
Single-gpu Inference
We list the height/width/frame settings we support in the following table.
| Resolution | h/w=9:16 | h/w=16:9 | h/w=4:3 | h/w=3:4 | h/w=1:1 |
|---|
| 540p | 544px960px129f | 960px544px129f | 624px832px129f | 832px624px129f | 720px720px129f |
| 720p (recommended) | 720px1280px129f | 1280px720px129f | 1104px832px129f | 832px1104px129f | 960px960px129f |
Using Command Line
1cd HunyuanVideo
2
3python3 sample_video.py \
4 --video-size 720 1280 \
5 --video-length 129 \
6 --infer-steps 50 \
7 --prompt "A cat walks on the grass, realistic style." \
8 --flow-reverse \
9 --use-cpu-offload \
10 --save-path ./results
Run a Gradio Server
1python3 gradio_server.py --flow-reverse
2
3# set SERVER_NAME and SERVER_PORT manually
4# SERVER_NAME=0.0.0.0 SERVER_PORT=8081 python3 gradio_server.py --flow-reverse
More Configurations
We list some more useful configurations for easy usage:
| Argument | Default | Description |
|---|
--prompt | None | The text prompt for video generation |
--video-size | 720 1280 | The size of the generated video |
--video-length | 129 | The length of the generated video |
--infer-steps | 50 | The number of steps for sampling |
--embedded-cfg-scale | 6.0 | Embedded Classifier free guidance scale |
--flow-shift | 7.0 | Shift factor for flow matching schedulers |
--flow-reverse | False | If reverse, learning/sampling from t=1 -> t=0 |
--seed | None | The random seed for generating video, if None, we init a random seed |
--use-cpu-offload | False | Use CPU offload for the model load to save more memory, necessary for high-res video generation |
--save-path | ./results | Path to save the generated video |
Parallel Inference on Multiple GPUs by xDiT
xDiT is a Scalable Inference Engine for Diffusion Transformers (DiTs) on multi-GPU Clusters.
It has successfully provided low-latency parallel inference solutions for a variety of DiTs models, including mochi-1, CogVideoX, Flux.1, SD3, etc. This repo adopted the
Unified Sequence Parallelism (USP) APIs for parallel inference of the HunyuanVideo model.
Using Command Line
For example, to generate a video with 8 GPUs, you can use the following command:
1cd HunyuanVideo
2
3torchrun --nproc_per_node=8 sample_video.py \
4 --video-size 1280 720 \
5 --video-length 129 \
6 --infer-steps 50 \
7 --prompt "A cat walks on the grass, realistic style." \
8 --flow-reverse \
9 --seed 42 \
10 --ulysses-degree 8 \
11 --ring-degree 1 \
12 --save-path ./results
You can change the --ulysses-degree and --ring-degree to control the parallel configurations for the best performance. The valid parallel configurations are shown in the following table.
Supported Parallel Configurations (Click to expand)
| --video-size | --video-length | --ulysses-degree x --ring-degree | --nproc_per_node |
|---|
| 1280 720 or 720 1280 | 129 | 8x1,4x2,2x4,1x8 | 8 |
| 1280 720 or 720 1280 | 129 | 1x5 | 5 |
| 1280 720 or 720 1280 | 129 | 4x1,2x2,1x4 | 4 |
| 1280 720 or 720 1280 | 129 | 3x1,1x3 | 3 |
| 1280 720 or 720 1280 | 129 | 2x1,1x2 | 2 |
| 1104 832 or 832 1104 | 129 | 4x1,2x2,1x4 | 4 |
| 1104 832 or 832 1104 | 129 | 3x1,1x3 | 3 |
| 1104 832 or 832 1104 | 129 | 2x1,1x2 | 2 |
| 960 960 | 129 | 6x1,3x2,2x3,1x6 | 6 |
| 960 960 | 129 | 4x1,2x2,1x4 | 4 |
| 960 960 | 129 | 3x1,1x3 | 3 |
| 960 960 | 129 | 1x2,2x1 | 2 |
| 960 544 or 544 960 | 129 | 6x1,3x2,2x3,1x6 | 6 |
| 960 544 or 544 960 | 129 | 4x1,2x2,1x4 | 4 |
| 960 544 or 544 960 | 129 | 3x1,1x3 | 3 |
| 960 544 or 544 960 | 129 | 1x2,2x1 | 2 |
| 832 624 or 624 832 | 129 | 4x1,2x2,1x4 | 4 |
| 624 832 or 624 832 | 129 | 3x1,1x3 | 3 |
| 832 624 or 624 832 | 129 | 2x1,1x2 | 2 |
| 720 720 | 129 | 1x5 | 5 |
| 720 720 | 129 | 3x1,1x3 | 3 |
| Latency (Sec) for 1280x720 (129 frames 50 steps) on 8xGPU |
|---|
| 1 | 2 | 4 | 8 |
|---|
| 1904.08 | 934.09 (2.04x) | 514.08 (3.70x) | 337.58 (5.64x) |
|---|
FP8 Inference
Using HunyuanVideo with FP8 quantized weights, which saves about 10GB of GPU memory. You can download the
weights and
weight scales from Huggingface.
Using Command Line
Here, you must explicitly specify the FP8 weight path. For example, to generate a video with fp8 weights, you can use the following command:
1cd HunyuanVideo
2
3DIT_CKPT_PATH={PATH_TO_FP8_WEIGHTS}/{WEIGHT_NAME}_fp8.pt
4
5python3 sample_video.py \
6 --dit-weight ${DIT_CKPT_PATH} \
7 --video-size 1280 720 \
8 --video-length 129 \
9 --infer-steps 50 \
10 --prompt "A cat walks on the grass, realistic style." \
11 --seed 42 \
12 --embedded-cfg-scale 6.0 \
13 --flow-shift 7.0 \
14 --flow-reverse \
15 --use-cpu-offload \
16 --use-fp8 \
17 --save-path ./results
BibTeX
If you find
HunyuanVideo useful for your research and applications, please cite using this BibTeX:
1@misc{kong2024hunyuanvideo,
2 title={HunyuanVideo: A Systematic Framework For Large Video Generative Models},
3 author={Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai, Jianbing Wu, Jinbao Xue, Joey Wang, Junkun Yuan, Kai Wang, Mengyang Liu, Pengyu Li, Shuai Li, Weiyan Wang, Wenqing Yu, Xinchi Deng, Yang Li, Yanxin Long, Yi Chen, Yutao Cui, Yuanbo Peng, Zhentao Yu, Zhiyu He, Zhiyong Xu, Zixiang Zhou, Zunnan Xu, Yangyu Tao, Qinglin Lu, Songtao Liu, Dax Zhou, Hongfa Wang, Yong Yang, Di Wang, Yuhong Liu, and Jie Jiang, along with Caesar Zhong},
4 year={2024},
5 archivePrefix={arXiv preprint arXiv:2412.03603},
6 primaryClass={cs.CV},
7 url={https://arxiv.org/abs/2412.03603},
8}
Acknowledgements
We would like to thank the contributors to the
SD3,
FLUX,
Llama,
LLaVA,
Xtuner,
diffusers and
HuggingFace repositories, for their open research and exploration.
Additionally, we also thank the Tencent Hunyuan Multimodal team for their help with the text encoder.
