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212 lines
8.0 KiB
Markdown
212 lines
8.0 KiB
Markdown
# AnimateDiff
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This repository is the official implementation of [AnimateDiff](https://arxiv.org/abs/2307.04725).
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**[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)**
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</br>
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Yuwei Guo,
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Ceyuan Yang*,
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Anyi Rao,
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Yaohui Wang,
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Yu Qiao,
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Dahua Lin,
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Bo Dai
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<p style="font-size: 0.8em; margin-top: -1em">*Corresponding Author</p>
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[Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/)
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## Todo
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- [x] Code Release
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- [x] Arxiv Report
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- [x] GPU Memory Optimization
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- [ ] Gradio Interface
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## Setup for Inference
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### Prepare Environment
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~~Our approach takes around 60 GB GPU memory to inference. NVIDIA A100 is recommanded.~~
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***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 !!***
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```
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git clone https://github.com/guoyww/AnimateDiff.git
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cd AnimateDiff
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conda env create -f environment.yaml
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conda activate animatediff
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```
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### Download Base T2I & Motion Module Checkpoints
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We provide two versions of our Motion Module, which are trained on stable-diffusion-v1-4 and finetuned on v1-5 seperately.
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It's recommanded to try both of them for best results.
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```
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git lfs install
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git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/
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bash download_bashscripts/0-MotionModule.sh
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```
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You may also directly download the motion module checkpoints from [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing), then put them in `models/Motion_Module/` folder.
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### Prepare Personalize T2I
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Here we provide inference configs for 6 demo T2I on CivitAI.
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You may run the following bash scripts to download these checkpoints.
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```
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bash download_bashscripts/1-ToonYou.sh
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bash download_bashscripts/2-Lyriel.sh
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bash download_bashscripts/3-RcnzCartoon.sh
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bash download_bashscripts/4-MajicMix.sh
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bash download_bashscripts/5-RealisticVision.sh
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bash download_bashscripts/6-Tusun.sh
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bash download_bashscripts/7-FilmVelvia.sh
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bash download_bashscripts/8-GhibliBackground.sh
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```
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### Inference
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After downloading the above peronalized T2I checkpoints, run the following commands to generate animations. The results will automatically be saved to `samples/` folder.
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```
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python -m scripts.animate --config configs/prompts/1-ToonYou.yaml
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python -m scripts.animate --config configs/prompts/2-Lyriel.yaml
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python -m scripts.animate --config configs/prompts/3-RcnzCartoon.yaml
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python -m scripts.animate --config configs/prompts/4-MajicMix.yaml
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python -m scripts.animate --config configs/prompts/5-RealisticVision.yaml
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python -m scripts.animate --config configs/prompts/6-Tusun.yaml
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python -m scripts.animate --config configs/prompts/7-FilmVelvia.yaml
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python -m scripts.animate --config configs/prompts/8-GhibliBackground.yaml
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```
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To generate animations with a new DreamBooth/LoRA model, you may create a new config `.yaml` file in the following format:
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```
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NewModel:
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path: "[path to your DreamBooth/LoRA model .safetensors file]"
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base: "[path to LoRA base model .safetensors file, leave it empty string if not needed]"
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motion_module:
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- "models/Motion_Module/mm_sd_v14.ckpt"
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- "models/Motion_Module/mm_sd_v15.ckpt"
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steps: 25
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guidance_scale: 7.5
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prompt:
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- "[positive prompt]"
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n_prompt:
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- "[negative prompt]"
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```
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Then run the following commands:
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```
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python -m scripts.animate --config [path to the config file]
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```
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## Gallery
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Here we demonstrate several best results we found in our experiments.
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<table class="center">
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<tr>
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<td><img src="__assets__/animations/model_01/01.gif"></td>
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<td><img src="__assets__/animations/model_01/02.gif"></td>
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<td><img src="__assets__/animations/model_01/03.gif"></td>
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<td><img src="__assets__/animations/model_01/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/30240/toonyou">ToonYou</a></p>
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<table>
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<tr>
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<td><img src="__assets__/animations/model_02/01.gif"></td>
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<td><img src="__assets__/animations/model_02/02.gif"></td>
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<td><img src="__assets__/animations/model_02/03.gif"></td>
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<td><img src="__assets__/animations/model_02/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/4468/counterfeit-v30">Counterfeit V3.0</a></p>
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<table>
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<tr>
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<td><img src="__assets__/animations/model_03/01.gif"></td>
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<td><img src="__assets__/animations/model_03/02.gif"></td>
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<td><img src="__assets__/animations/model_03/03.gif"></td>
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<td><img src="__assets__/animations/model_03/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/4201/realistic-vision-v20">Realistic Vision V2.0</a></p>
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<table>
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<tr>
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<td><img src="__assets__/animations/model_04/01.gif"></td>
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<td><img src="__assets__/animations/model_04/02.gif"></td>
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<td><img src="__assets__/animations/model_04/03.gif"></td>
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<td><img src="__assets__/animations/model_04/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">Model： <a href="https://civitai.com/models/43331/majicmix-realistic">majicMIX Realistic</a></p>
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<table>
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<tr>
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<td><img src="__assets__/animations/model_05/01.gif"></td>
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<td><img src="__assets__/animations/model_05/02.gif"></td>
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<td><img src="__assets__/animations/model_05/03.gif"></td>
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<td><img src="__assets__/animations/model_05/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/66347/rcnz-cartoon-3d">RCNZ Cartoon</a></p>
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<table>
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<tr>
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<td><img src="__assets__/animations/model_06/01.gif"></td>
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<td><img src="__assets__/animations/model_06/02.gif"></td>
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<td><img src="__assets__/animations/model_06/03.gif"></td>
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<td><img src="__assets__/animations/model_06/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/33208/filmgirl-film-grain-lora-and-loha">FilmVelvia</a></p>
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#### Community Cases
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Here are some samples contributed by the community artists. Create a Pull Request if you would like to show your results here😚.
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<table>
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<tr>
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<td><img src="__assets__/animations/model_07/init.jpg"></td>
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<td><img src="__assets__/animations/model_07/01.gif"></td>
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<td><img src="__assets__/animations/model_07/02.gif"></td>
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<td><img src="__assets__/animations/model_07/03.gif"></td>
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<td><img src="__assets__/animations/model_07/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">
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Character Model：<a href="https://civitai.com/models/13237/genshen-impact-yoimiya">Yoimiya</a>
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(with an initial reference image, see <a href="https://github.com/talesofai/AnimateDiff">WIP fork</a> for the extended implementation.)
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<table>
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<tr>
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<td><img src="__assets__/animations/model_08/01.gif"></td>
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<td><img src="__assets__/animations/model_08/02.gif"></td>
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<td><img src="__assets__/animations/model_08/03.gif"></td>
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<td><img src="__assets__/animations/model_08/04.gif"></td>
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</tr>
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</table>
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<p style="margin-left: 2em; margin-top: -1em">
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Character Model：<a href="https://civitai.com/models/9850/paimon-genshin-impact">Paimon</a>;
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Pose Model：<a href="https://civitai.com/models/107295/or-holdingsign">Hold Sign</a></p>
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## BibTeX
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```
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@misc{guo2023animatediff,
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title={AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning},
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author={Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai},
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year={2023},
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eprint={2307.04725},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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## Contact Us
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**Yuwei Guo**: [guoyuwei@pjlab.org.cn](mailto:guoyuwei@pjlab.org.cn)
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**Ceyuan Yang**: [yangceyuan@pjlab.org.cn](mailto:yangceyuan@pjlab.org.cn)
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**Bo Dai**: [daibo@pjlab.org.cn](mailto:daibo@pjlab.org.cn)
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## Acknowledgements
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Codebase built upon [Tune-a-Video](https://github.com/showlab/Tune-A-Video). |