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README.md
README.md
 
 

README.md

Download Pretrained Models

All models are stored in HunyuanVideo/ckpts by default, and the file structure is as follows

HunyuanVideo
  ├──ckpts
  │  ├──README.md
  │  ├──hunyuan-video-t2v-720p
  │  │  ├──transformers
  ├  │  ├──vae
  │  ├──text_encoder
  │  ├──text_encoder_2
  ├──...

Download HunyuanVideo model

To download the HunyuanVideo model, first install the huggingface-cli. (Detailed instructions are available here.)

python -m pip install "huggingface_hub[cli]"

Then download the model using the following commands:

# Switch to the directory named 'HunyuanVideo'
cd HunyuanVideo
# Use the huggingface-cli tool to download HunyuanVideo model in HunyuanVideo/ckpts dir.
# The download time may vary from 10 minutes to 1 hour depending on network conditions.
huggingface-cli download tencent/HunyuanVideo --local-dir ./ckpts
💡Tips for using huggingface-cli (network problem)
1. Using HF-Mirror

If you encounter slow download speeds in China, you can try a mirror to speed up the download process. For example,

HF_ENDPOINT=https://hf-mirror.com huggingface-cli download tencent/HunyuanVideo --local-dir ./ckpts
2. Resume Download

huggingface-cli supports resuming downloads. If the download is interrupted, you can just rerun the download command to resume the download process.

Note: If an No such file or directory: 'ckpts/.huggingface/.gitignore.lock' like error occurs during the download process, you can ignore the error and rerun the download command.

Download Text Encoder

HunyuanVideo uses an MLLM model and a CLIP model as text encoder.

  1. MLLM model (text_encoder folder)

HunyuanVideo supports different MLLMs (including HunyuanMLLM and open-source MLLM models). At this stage, we have not yet released HunyuanMLLM. We recommend the user in community to use llava-llama-3-8b provided by Xtuer, which can be downloaded by the following command

cd HunyuanVideo/ckpts
huggingface-cli download xtuner/llava-llama-3-8b-v1_1-transformers --local-dir ./llava-llama-3-8b-v1_1-transformers

In order to save GPU memory resources for model loading, we separate the language model parts of llava-llama-3-8b-v1_1-transformers into text_encoder.

cd HunyuanVideo
python hyvideo/utils/preprocess_text_encoder_tokenizer_utils.py --input_dir ckpts/llava-llama-3-8b-v1_1-transformers --output_dir ckpts/text_encoder
  1. CLIP model (text_encoder_2 folder)

We use CLIP provided by OpenAI as another text encoder, users in the community can download this model by the following command

cd HunyuanVideo/ckpts
huggingface-cli download openai/clip-vit-large-patch14 --local-dir ./text_encoder_2