Upload README.md

README.md

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+# Download Pretrained Models
+
+All models are stored in `HunyuanVideo/ckpts` by default, and the file structure is as follows
+```shell
+HunyuanVideo
+  ├──ckpts
+  │  ├──README.md
+  │  ├──hunyuan-video-t2v-720p
+  │  │  ├──transformers
+  │  │  │  ├──mp_rank_00_model_states.pt
+  │  │  │  ├──mp_rank_00_model_states_fp8.pt
+  │  │  │  ├──mp_rank_00_model_states_fp8_map.pt
+  ├  │  ├──vae
+  │  ├──text_encoder
+  │  ├──text_encoder_2
+  ├──...
+```
+
+## Download HunyuanVideo model
+To download the HunyuanVideo model, first install the huggingface-cli. (Detailed instructions are available [here](https://huggingface.co/docs/huggingface_hub/guides/cli).)
+
+```shell
+python -m pip install "huggingface_hub[cli]"
+```
+
+Then download the model using the following commands:
+
+```shell
+# 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
+```
+
+<details>
+<summary>💡Tips for using huggingface-cli (network problem)</summary>
+
+##### 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,
+
+```shell
+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.
+
+</details>
+
+---
+
+## 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](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers) provided by [Xtuer](https://huggingface.co/xtuner), which can be downloaded by the following command
+
+```shell
+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
+```
+
+2. CLIP model (text_encoder_2 folder)
+
+We use [CLIP](https://huggingface.co/openai/clip-vit-large-patch14) provided by [OpenAI](https://openai.com) 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
+```