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, 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

Gallery

Here we demonstrate several best results we found in our experiments or generated by other artists.

Model：ToonYou

Model：Counterfeit V3.0

Model：Realistic Vision V2.0

Model： majicMIX Realistic

Model：RCNZ Cartoon

Model：FilmVelvia

Community Cases

See WIP fork for some extended implementation.

Character Model：Yoimiya
Along with an initial reference image

Character Model：Paimon along with Pose Model：Hold Sign

BibTeX

@misc{guo2023animatediff,
      title={AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning}, 
      author={Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai},
      year={2023},
      eprint={2307.04725},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Contact Us

Yuwei Guo: guoyuwei@pjlab.org.cn
Ceyuan Yang: yangceyuan@pjlab.org.cn
Bo Dai: daibo@pjlab.org.cn

Acknowledgements

Codebase built upon Tune-a-Video.

README.md Unescape Escape

AnimateDiff

Todo

Setup for Inference

Prepare Environment