__assets__/docs/animatediff.md

# AnimateDiff: training and inference setup
## Setups for Inference

### Prepare Environment

***We updated our inference code with xformers and a sequential decoding trick. Now AnimateDiff takes only ~12GB VRAM to inference, and run on a single RTX3090 !!***

```
git clone https://github.com/guoyww/AnimateDiff.git
cd AnimateDiff

conda env create -f environment.yaml
conda activate animatediff
```

### Download Base T2I & Motion Module Checkpoints
We provide two versions of our Motion Module, which are trained on stable-diffusion-v1-4 and finetuned on v1-5 seperately.
It's recommanded to try both of them for best results.
```
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/

bash download_bashscripts/0-MotionModule.sh
```
You may also directly download the motion module checkpoints from [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) / [HuggingFace](https://huggingface.co/guoyww/animatediff) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules), then put them in `models/Motion_Module/` folder.

### Prepare Personalize T2I
Here we provide inference configs for 6 demo T2I on CivitAI.
You may run the following bash scripts to download these checkpoints.
```
bash download_bashscripts/1-ToonYou.sh
bash download_bashscripts/2-Lyriel.sh
bash download_bashscripts/3-RcnzCartoon.sh
bash download_bashscripts/4-MajicMix.sh
bash download_bashscripts/5-RealisticVision.sh
bash download_bashscripts/6-Tusun.sh
bash download_bashscripts/7-FilmVelvia.sh
bash download_bashscripts/8-GhibliBackground.sh
```

### Inference
After downloading the above peronalized T2I checkpoints, run the following commands to generate animations. The results will automatically be saved to `samples/` folder.
```
python -m scripts.animate --config configs/prompts/1-ToonYou.yaml
python -m scripts.animate --config configs/prompts/2-Lyriel.yaml
python -m scripts.animate --config configs/prompts/3-RcnzCartoon.yaml
python -m scripts.animate --config configs/prompts/4-MajicMix.yaml
python -m scripts.animate --config configs/prompts/5-RealisticVision.yaml
python -m scripts.animate --config configs/prompts/6-Tusun.yaml
python -m scripts.animate --config configs/prompts/7-FilmVelvia.yaml
python -m scripts.animate --config configs/prompts/8-GhibliBackground.yaml
```

To generate animations with a new DreamBooth/LoRA model, you may create a new config `.yaml` file in the following format:
```
NewModel:
  inference_config: "[path to motion module config file]"

  motion_module:
    - "models/Motion_Module/mm_sd_v14.ckpt"
    - "models/Motion_Module/mm_sd_v15.ckpt"
    
    motion_module_lora_configs:
    - path:  "[path to MotionLoRA model]"
      alpha: 1.0
    - ...

  dreambooth_path: "[path to your DreamBooth model .safetensors file]"
  lora_model_path: "[path to your LoRA model .safetensors file, leave it empty string if not needed]"

  steps:          25
  guidance_scale: 7.5

  prompt:
    - "[positive prompt]"

  n_prompt:
    - "[negative prompt]"
```
Then run the following commands:
```
python -m scripts.animate --config [path to the config file]
```


## Steps for Training

### Dataset
Before training, download the videos files and the `.csv` annotations of [WebVid10M](https://maxbain.com/webvid-dataset/) to the local mechine.
Note that our examplar training script requires all the videos to be saved in a single folder. You may change this by modifying `animatediff/data/dataset.py`.

### Configuration
After dataset preparations, update the below data paths in the config `.yaml` files in `configs/training/` folder:
```
train_data:
  csv_path:     [Replace with .csv Annotation File Path]
  video_folder: [Replace with Video Folder Path]
  sample_size:  256
```
Other training parameters (lr, epochs, validation settings, etc.) are also included in the config files.

### Training
To train motion modules
```
torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/training.yaml
```

To finetune the unet's image layers
```
torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/image_finetune.yaml
```