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Genshin_Impact_KAEDEHARA_KAZUHA 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 KAEDEHARA_KAZUHA. 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/Genshin_Impact_KAEDEHARA_KAZUHA_HunyuanVideo_lora
cd Genshin_Impact_KAEDEHARA_KAZUHA_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: KAEDEHARA_KAZUHA
Run the following command to generate a video of KAEDEHARA_KAZUHA:
python hv_generate_video.py \
--fp8 \
--video_size 544 960 \
--video_length 60 \
--infer_steps 30 \
--prompt "This is a digital anime-style illustration featuring KAEDEHARA KAZUHA, a character with long, flowing white hair with red streaks, and red eyes, leaning on a wooden table in a cozy, warmly-lit café. She wears a black and orange outfit with a red scarf. In the background, there are shelves with various items and soft lighting. On the table, there is a glass of orange juice. The atmosphere is calm and inviting." \
--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 KAEDEHARA_KAZUHA_im_lora_dir/KAEDEHARA_KAZUHA_single_im_lora-000008.safetensors
Generate Video 2: KAEDEHARA_KAZUHA Letter
Run the following command to generate a video of KAEDEHARA_KAZUHA:
python hv_generate_video.py \
--fp8 \
--video_size 544 960 \
--video_length 60 \
--infer_steps 30 \
--prompt "In this digital anime-style artwork, KAEDEHARA KAZUHA, a young man with silver hair and red eyes, is holding a white envelope with Japanese text. He wears a white T-shirt and a red wristband. The background shows a warmly lit room with wooden furniture, a vase of autumn leaves, and a window casting soft light. The atmosphere is cozy and inviting." \
--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 KAEDEHARA_KAZUHA_im_lora_dir/KAEDEHARA_KAZUHA_single_im_lora-000008.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.