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Empress Dowager Cixi (In The Firmament of The Pleiades 《苍穹之昴》) HunyuanVideo LoRA
This repository contains the necessary setup and scripts to generate videos using the HunyuanVideo model with a LoRA (Low-Rank Adaptation) fine-tuned for Empress Dowager Cixi. Below are the instructions to install dependencies, download models, and run the demo.
Installation
Step 1: Install System Dependencies
Run the following command to install required system packages:
sudo apt-get update && sudo apt-get install git-lfs ffmpeg cbm
Step 2: Clone the Repository
Clone the repository and navigate to the project directory:
git clone https://huggingface.co/svjack/Empress_Dowager_Cixi_HunyuanVideo_lora
cd Empress_Dowager_Cixi_HunyuanVideo_lora
Step 3: Install Python Dependencies
Install the required Python packages:
conda create -n py310 python=3.10
conda activate py310
pip install ipykernel
python -m ipykernel install --user --name py310 --display-name "py310"
pip install -r requirements.txt
pip install ascii-magic matplotlib tensorboard huggingface_hub
pip install moviepy==1.0.3
pip install sageattention==1.0.6
pip install torch==2.5.0 torchvision
Download Models
Step 1: Download HunyuanVideo Model
Download the HunyuanVideo model and place it in the ckpts
directory:
huggingface-cli download tencent/HunyuanVideo --local-dir ./ckpts
Step 2: Download LLaVA Model
Download the LLaVA model and preprocess it:
cd ckpts
huggingface-cli download xtuner/llava-llama-3-8b-v1_1-transformers --local-dir ./llava-llama-3-8b-v1_1-transformers
wget https://raw.githubusercontent.com/Tencent/HunyuanVideo/refs/heads/main/hyvideo/utils/preprocess_text_encoder_tokenizer_utils.py
python preprocess_text_encoder_tokenizer_utils.py --input_dir llava-llama-3-8b-v1_1-transformers --output_dir text_encoder
Step 3: Download CLIP Model
Download the CLIP model for the text encoder:
huggingface-cli download openai/clip-vit-large-patch14 --local-dir ./text_encoder_2
Demo
Generate Video 1: Empress Dowager Cixi
Run the following command to generate a video of Prince Kim Hyesung:
python hv_generate_video.py \
--fp8 \
--video_size 544 960 \
--video_length 60 \
--infer_steps 30 \
--prompt "featuring Empress Dowager Cixi in traditional Chinese attire. She has long black hair tied back, and is adorned with ornate, dangling earrings and a golden necklace. Her expression is serene, with closed eyes and a slight smile. The background is softly lit, with a blurred, elegant interior featuring a window with red and gold accents, and traditional Chinese decor." \
--save_path . \
--output_type both \
--dit ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
--attn_mode sdpa \
--vae ckpts/hunyuan-video-t2v-720p/vae/pytorch_model.pt \
--vae_chunk_size 32 \
--vae_spatial_tile_sample_min_size 128 \
--text_encoder1 ckpts/text_encoder \
--text_encoder2 ckpts/text_encoder_2 \
--seed 1234 \
--lora_multiplier 1.0 \
--lora_weight Cixi_im_lora_dir/Cixi_im_lora-000005.safetensors
Generate Video 2: Empress Dowager Cixi Tea
Run the following command to generate a video of Prince Kim Hyesung:
python hv_generate_video.py \
--fp8 \
--video_size 544 960 \
--video_length 60 \
--infer_steps 30 \
--prompt "Empress Dowager Cixi sits gracefully, dressed in traditional Chinese attire, enjoying a moment of tea. Her long black hair is neatly tied back, and she is adorned with ornate, dangling earrings and a golden necklace. Her expression is serene, with closed eyes and a slight smile, as she savors the tea. The softly lit background reveals a blurred, elegant interior featuring a window with red and gold accents, and traditional Chinese decor, adding to the tranquil atmosphere of the scene." \
--save_path . \
--output_type both \
--dit ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
--attn_mode sdpa \
--vae ckpts/hunyuan-video-t2v-720p/vae/pytorch_model.pt \
--vae_chunk_size 32 \
--vae_spatial_tile_sample_min_size 128 \
--text_encoder1 ckpts/text_encoder \
--text_encoder2 ckpts/text_encoder_2 \
--seed 1234 \
--lora_multiplier 1.0 \
--lora_weight Cixi_im_lora_dir/Cixi_im_lora-000015.safetensors
Notes
- Ensure you have sufficient GPU resources for video generation.
- Adjust the
--video_size
,--video_length
, and--infer_steps
parameters as needed for different output qualities and lengths. - The
--prompt
parameter can be modified to generate videos with different scenes or actions.
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.