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# FramePack Dancing Image-to-Video Generation

This repository contains the necessary steps and scripts to generate videos using the Dancing image-to-video model. The model leverages LoRA (Low-Rank Adaptation) weights and pre-trained components to create high-quality anime-style videos based on textual prompts.

## Prerequisites

Before proceeding, ensure that you have the following installed on your system:

• **Ubuntu** (or a compatible Linux distribution)
• **Python 3.x**
• **pip** (Python package manager)
• **Git**
• **Git LFS** (Git Large File Storage)
• **FFmpeg**

## Installation

1. **Update and Install Dependencies**

   ```bash
   sudo apt-get update && sudo apt-get install cbm git-lfs ffmpeg
   ```

2. **Clone the Repository**

   ```bash
   git clone https://huggingface.co/svjack/YiChen_FramePack_lora_early
   cd YiChen_FramePack_lora_early
   ```

3. **Install Python Dependencies**

   ```bash
   pip install torch torchvision
   pip install -r requirements.txt
   pip install ascii-magic matplotlib tensorboard huggingface_hub datasets
   pip install moviepy==1.0.3
   pip install sageattention==1.0.6
   ```

4. **Download Model Weights**

   ```bash
    git clone https://huggingface.co/lllyasviel/FramePackI2V_HY
    git clone https://huggingface.co/hunyuanvideo-community/HunyuanVideo
    git clone https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged
    git clone https://huggingface.co/Comfy-Org/sigclip_vision_384
   ```

## Usage

To generate a video, use the `fpack_generate_video.py` script with the appropriate parameters. Below are examples of how to generate videos using the Dancing model.



### 1. Furina
- Source Image


```bash
python fpack_generate_video.py \
    --dit FramePackI2V_HY/diffusion_pytorch_model-00001-of-00003.safetensors \
    --vae HunyuanVideo/vae/diffusion_pytorch_model.safetensors \
    --text_encoder1 HunyuanVideo_repackaged/split_files/text_encoders/llava_llama3_fp16.safetensors \
    --text_encoder2 HunyuanVideo_repackaged/split_files/text_encoders/clip_l.safetensors \
    --image_encoder sigclip_vision_384/sigclip_vision_patch14_384.safetensors \
    --image_path fln.png \
    --prompt "In the style of Yi Chen Dancing White Background , The character's movements shift dynamically throughout the video, transitioning from poised stillness to lively dance steps. Her expressions evolve seamlessly—starting with focused determination, then flashing surprise as she executes a quick spin, before breaking into a joyful smile mid-leap. Her hands flow through choreographed positions, sometimes extending gracefully like unfolding wings, other times clapping rhythmically against her wrists. During a dramatic hip sway, her fingers fan open near her cheek, then sweep downward as her whole body dips into a playful crouch, the sequins on her costume catching the light with every motion." \
    --video_size 960 544 --video_seconds 3 --fps 30 --infer_steps 25 \
    --attn_mode sdpa --fp8_scaled \
    --vae_chunk_size 32 --vae_spatial_tile_sample_min_size 128 \
    --save_path save --output_type both \
    --seed 1234 --lora_multiplier 1.0 --lora_weight framepack_yichen_output/framepack-yichen-lora-000006.safetensors


```

- Without Lora

- With Lora


### 2. Roper 
- Source Image



```bash
python fpack_generate_video.py \
    --dit FramePackI2V_HY/diffusion_pytorch_model-00001-of-00003.safetensors \
    --vae HunyuanVideo/vae/diffusion_pytorch_model.safetensors \
    --text_encoder1 HunyuanVideo_repackaged/split_files/text_encoders/llava_llama3_fp16.safetensors \
    --text_encoder2 HunyuanVideo_repackaged/split_files/text_encoders/clip_l.safetensors \
    --image_encoder sigclip_vision_384/sigclip_vision_patch14_384.safetensors \
    --image_path shengjiang.png \
    --prompt "In the style of Yi Chen Dancing White Background , The character's movements shift dynamically throughout the video, transitioning from poised stillness to lively dance steps. Her expressions evolve seamlessly—starting with focused determination, then flashing surprise as she executes a quick spin, before breaking into a joyful smile mid-leap. Her hands flow through choreographed positions, sometimes extending gracefully like unfolding wings, other times clapping rhythmically against her wrists. During a dramatic hip sway, her fingers fan open near her cheek, then sweep downward as her whole body dips into a playful crouch, the sequins on her costume catching the light with every motion." \
    --video_size 960 544 --video_seconds 3 --fps 30 --infer_steps 25 \
    --attn_mode sdpa --fp8_scaled \
    --vae_chunk_size 32 --vae_spatial_tile_sample_min_size 128 \
    --save_path save --output_type both \
    --seed 1234 --lora_multiplier 1.0 --lora_weight framepack_yichen_output/framepack-yichen-lora-000006.safetensors

``` 

- With Lora



### 3. Varesa
- Source Image


```bash
python fpack_generate_video.py \
    --dit FramePackI2V_HY/diffusion_pytorch_model-00001-of-00003.safetensors \
    --vae HunyuanVideo/vae/diffusion_pytorch_model.safetensors \
    --text_encoder1 HunyuanVideo_repackaged/split_files/text_encoders/llava_llama3_fp16.safetensors \
    --text_encoder2 HunyuanVideo_repackaged/split_files/text_encoders/clip_l.safetensors \
    --image_encoder sigclip_vision_384/sigclip_vision_patch14_384.safetensors \
    --image_path waliesha.jpg \
    --prompt "In the style of Yi Chen Dancing White Background , The dancer’s energy pulses in waves—one moment a statue, poised and precise, the next a whirl of motion as her feet flicker across the floor. Her face tells its own story: brows knit in concentration, then eyes widening mid-turn as if startled by her own speed, before dissolving into laughter as she springs upward, weightless. Her arms carve the air—now arcing like ribbons unfurling, now snapping sharp as a whip’s crack, palms meeting wrists in staccato beats. A roll of her hips sends her fingers fluttering near her temple, then cascading down as she folds into a teasing dip, the beads on her dress scattering light like sparks." \
    --video_size 960 544 --video_seconds 3 --fps 30 --infer_steps 25 \
    --attn_mode sdpa --fp8_scaled \
    --vae_chunk_size 32 --vae_spatial_tile_sample_min_size 128 \
    --save_path save --output_type both \
    --seed 1234 --lora_multiplier 1.0 --lora_weight framepack_yichen_output/framepack-yichen-lora-000006.safetensors

```
- With Lora



### 4. Scaramouche  
- Source Image


```bash
python fpack_generate_video.py \
    --dit FramePackI2V_HY/diffusion_pytorch_model-00001-of-00003.safetensors \
    --vae HunyuanVideo/vae/diffusion_pytorch_model.safetensors \
    --text_encoder1 HunyuanVideo_repackaged/split_files/text_encoders/llava_llama3_fp16.safetensors \
    --text_encoder2 HunyuanVideo_repackaged/split_files/text_encoders/clip_l.safetensors \
    --image_encoder sigclip_vision_384/sigclip_vision_patch14_384.safetensors \
    --image_path shanbing.jpg \
    --prompt "In the style of Yi Chen Dancing White Background , The dancer’s energy pulses in waves—one moment a statue, poised and precise, the next a whirl of motion as her feet flicker across the floor. Her face tells its own story: brows knit in concentration, then eyes widening mid-turn as if startled by her own speed, before dissolving into laughter as she springs upward, weightless. Her arms carve the air—now arcing like ribbons unfurling, now snapping sharp as a whip’s crack, palms meeting wrists in staccato beats. A roll of her hips sends her fingers fluttering near her temple, then cascading down as she folds into a teasing dip, the beads on her dress scattering light like sparks." \
    --video_size 960 544 --video_seconds 3 --fps 30 --infer_steps 25 \
    --attn_mode sdpa --fp8_scaled \
    --vae_chunk_size 32 --vae_spatial_tile_sample_min_size 128 \
    --save_path save --output_type both \
    --seed 1234 --lora_multiplier 1.0 --lora_weight framepack_yichen_output/framepack-yichen-lora-000006.safetensors

```

- With Lora




## Parameters

* `--fp8`: Enable FP8 precision (optional).
* `--task`: Specify the task (e.g., `t2v-1.3B`).
* `--video_size`: Set the resolution of the generated video (e.g., `1024 1024`).
* `--video_length`: Define the length of the video in frames.
* `--infer_steps`: Number of inference steps.
* `--save_path`: Directory to save the generated video.
* `--output_type`: Output type (e.g., `both` for video and frames).
* `--dit`: Path to the diffusion model weights.
* `--vae`: Path to the VAE model weights.
* `--t5`: Path to the T5 model weights.
* `--attn_mode`: Attention mode (e.g., `torch`).
* `--lora_weight`: Path to the LoRA weights.
* `--lora_multiplier`: Multiplier for LoRA weights.
* `--prompt`: Textual prompt for video generation.



## Output

The generated video and frames will be saved in the specified `save_path` directory.

## Troubleshooting

• Ensure all dependencies are correctly installed.
• Verify that the model weights are downloaded and placed in the correct locations.
• Check for any missing Python packages and install them using `pip`.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Acknowledgments

• **Hugging Face** for hosting the model weights.
• **Wan-AI** for providing the pre-trained models.
• **DeepBeepMeep** for contributing to the model weights.

## Contact

For any questions or issues, please open an issue on the repository or contact the maintainer.

---