This repository is the official implementation of AnimateDiff.
AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
Yuwei Guo,
Ceyuan Yang*,
Anyi Rao,
Yaohui Wang,
Yu Qiao,
Dahua Lin,
Bo Dai
*Corresponding Author
- Code Release
- Arxiv Report
- GPU Memory Optimization
- Gradio Interface
Our approach takes around 60 GB GPU memory to inference. NVIDIA A100 is recommanded.
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
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.
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
bash download_bashscripts/9-AdditionalNetworks.sh
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
python -m scripts.animate --config configs/prompts/9-AdditionalNetworks.yml
Here we demonstrate several best results we found in our experiments or generated by other artists.
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Model：ToonYou
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Model：Counterfeit V3.0
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Model：Realistic Vision V2.0
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Model： majicMIX Realistic
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Model：RCNZ Cartoon
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Model：FilmVelvia
See WIP fork for some extended implementation.
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Character Model：Yoimiya
Along with an initial reference image
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Character Model：Paimon along with Pose Model：Hold Sign
@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}
}
Yuwei Guo: [email protected]
Ceyuan Yang: [email protected]
Bo Dai: [email protected]
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