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 / HuggingFace / CivitAI, 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 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

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