-----
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](https://aivideo.hunyuan.tencent.com).
> [**HunyuanVideo: A Systematic Framework For Large Video Generation Model**](https://arxiv.org/abs/2412.03603)
## π₯π₯π₯ News!!
* Dec 18, 2024: πββοΈ We release the [FP8 model weights](https://huggingface.co/tencent/HunyuanVideo/blob/main/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt) of HunyuanVideo to save more GPU memory.
* Dec 17, 2024: π€ HunyuanVideo has been integrated into [Diffusers](https://huggingface.co/docs/diffusers/main/api/pipelines/hunyuan_video).
* Dec 7, 2024: π We release the parallel inference code for HunyuanVideo powered by [xDiT](https://github.com/xdit-project/xDiT).
* Dec 3, 2024: π We release the inference code and model weights of HunyuanVideo. [Download](https://github.com/Tencent/HunyuanVideo/blob/main/ckpts/README.md).
## π₯ Demo
## π **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 (Partially referred to [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan) ) 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](https://github.com/Tencent/Tencent-Hunyuan-Large) 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](hyvideo/prompt_rewrite.py). 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](https://github.com/Tencent/Tencent-Hunyuan-Large). We release the weights of the Prompt Rewrite Model [here](https://huggingface.co/Tencent/HunyuanVideo-PromptRewrite).
## π 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)
β
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
## π 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
## π οΈ Dependencies and Installation
Begin by cloning the repository:
```shell
git clone https://github.com/tencent/HunyuanVideo
cd 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](https://docs.anaconda.com/free/miniconda/index.html).
```shell
# 1. Create conda environment
conda create -n HunyuanVideo python==3.10.9
# 2. Activate the environment
conda activate HunyuanVideo
# 3. Install PyTorch and other dependencies using conda
# For CUDA 11.8
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=11.8 -c pytorch -c nvidia
# For CUDA 12.4
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
# 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@v2.6.3
# 6. Install xDiT for parallel inference (It is recommended to use torch 2.4.0 and flash-attn 2.6.3)
python -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:
```shell
# 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).
pip install nvidia-cublas-cu12==12.4.5.8
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
# Option 2: Forcing to explictly use the CUDA 11.8 compiled version of Pytorch and all the other packages
pip uninstall -r requirements.txt # uninstall all packages
pip uninstall -y xfuser
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install ninja
pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
pip 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.
```shell
# 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 hunyuanvideo/hunyuanvideo:cuda_11
```
## 𧱠Download Pretrained Models
The details of download pretrained models are shown [here](ckpts/README.md).
## π 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
```bash
cd HunyuanVideo
python3 sample_video.py \
--video-size 720 1280 \
--video-length 129 \
--infer-steps 50 \
--prompt "A cat walks on the grass, realistic style." \
--flow-reverse \
--use-cpu-offload \
--save-path ./results
```
### Run a Gradio Server
```bash
python3 gradio_server.py --flow-reverse
# set SERVER_NAME and SERVER_PORT manually
# 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 |
## π Parallel Inference on Multiple GPUs by xDiT
[xDiT](https://github.com/xdit-project/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)](https://arxiv.org/abs/2405.07719) 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:
```bash
cd HunyuanVideo
torchrun --nproc_per_node=8 sample_video.py \
--video-size 1280 720 \
--video-length 129 \
--infer-steps 50 \
--prompt "A cat walks on the grass, realistic style." \
--flow-reverse \
--seed 42 \
--ulysses-degree 8 \
--ring-degree 1 \
--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)
## π FP8 Inference
Using HunyuanVideo with FP8 quantized weights, which saves about 10GB of GPU memory. You can download the [weights](https://huggingface.co/tencent/HunyuanVideo/blob/main/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt) and [weight scales](https://huggingface.co/tencent/HunyuanVideo/blob/main/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8_map.pt) 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:
```bash
cd HunyuanVideo
DIT_CKPT_PATH={PATH_TO_FP8_WEIGHTS}/{WEIGHT_NAME}_fp8.pt
python3 sample_video.py \
--dit-weight ${DIT_CKPT_PATH} \
--video-size 1280 720 \
--video-length 129 \
--infer-steps 50 \
--prompt "A cat walks on the grass, realistic style." \
--seed 42 \
--embedded-cfg-scale 6.0 \
--flow-shift 7.0 \
--flow-reverse \
--use-cpu-offload \
--use-fp8 \
--save-path ./results
```
## π BibTeX
If you find [HunyuanVideo](https://arxiv.org/abs/2412.03603) useful for your research and applications, please cite using this BibTeX:
```BibTeX
@misc{kong2024hunyuanvideo,
title={HunyuanVideo: A Systematic Framework For Large Video Generative Models},
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, Daquan Zhou, Hongfa Wang, Yong Yang, Di Wang, Yuhong Liu, and Jie Jiang, along with Caesar Zhong},
year={2024},
archivePrefix={arXiv preprint arXiv:2412.03603},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.03603},
}
```
## Acknowledgements
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
Additionally, we also thank the Tencent Hunyuan Multimodal team for their help with the text encoder.
## Star History
&color=red)
[](https://replicate.com/zsxkib/hunyuan-video)
