PKU-YuanGroup / MagicTime

MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
https://pku-yuangroup.github.io/MagicTime/
Apache License 2.0
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diffusion-models long-video-generation metamorphic-video-generation open-sora-plan text-to-video time-lapse time-lapse-dataset video-generation

MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators

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This repository is the official implementation of MagicTime, a metamorphic video generation pipeline based on the given prompts. The main idea is to enhance the capacity of video generation models to accurately depict the real world through our proposed methods and dataset.

💡 We also have other video generation projects that may interest you ✨.

> [**Open-Sora-Plan**](https://github.com/PKU-YuanGroup/Open-Sora-Plan)
> PKU-Yuan Lab and Tuzhan AI etc.
> [![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [![github](https://img.shields.io/github/stars/PKU-YuanGroup/Open-Sora-Plan.svg?style=social)](https://github.com/PKU-YuanGroup/Open-Sora-Plan)
> > [**ConsisID**](https://arxiv.org/abs/2411.17440)
> Shenghai Yuan, Jinfa Huang and Xianyi He etc.
> [![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/PKU-YuanGroup/ConsisID/) [![github](https://img.shields.io/github/stars/PKU-YuanGroup/ConsisID.svg?style=social)](https://github.com/PKU-YuanGroup/ConsisID/)
> > [**ChronoMagic-Bench**](https://arxiv.org/abs/2406.18522)
> Shenghai Yuan, Jinfa Huang and Yongqi Xu etc.
> [![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/PKU-YuanGroup/ChronoMagic-Bench/) [![github](https://img.shields.io/github/stars/PKU-YuanGroup/ChronoMagic-Bench.svg?style=social)](https://github.com/PKU-YuanGroup/ChronoMagic-Bench/)
>

## 📣 News * ⏳⏳⏳ Training a stronger model with the support of [Open-Sora Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan). * ⏳⏳⏳ Release the training code of MagicTime. * ⏳⏳⏳ Integrate MagicTime into [Diffusers](https://huggingface.co/docs/diffusers/index). 🙏 **[Need your contribution]** * `[2024.07.29]` We add *batch inference* to [inference_magictime.py](https://github.com/PKU-YuanGroup/MagicTime/blob/main/inference_magictime.py) for easier usage. * `[2024.06.27]` Excited to share our latest [ChronoMagic-Bench](https://github.com/PKU-YuanGroup/ChronoMagic-Bench), a benchmark for metamorphic evaluation of text-to-time-lapse video generation, and is fully open source! Please check out the [paper](https://arxiv.org/abs/2406.18522). * `[2024.05.27]` Excited to share our latest Open-Sora Plan v1.1.0, which significantly improves video quality and length, and is fully open source! Please check out the [report](https://github.com/PKU-YuanGroup/Open-Sora-Plan/blob/main/docs/Report-v1.1.0.md). * `[2024.04.14]` Thanks [@camenduru](https://twitter.com/camenduru) and [@ModelsLab](https://modelslab.com/) for providing [Jupyter Notebook](https://github.com/camenduru/MagicTime-jupyter) and [Replicate Demo](https://replicate.com/camenduru/magictime). * `[2024.04.13]` 🔥 We have compressed the size of repo with less than 1.0 MB, so that everyone can clone easier and faster. You can click [here](https://github.com/PKU-YuanGroup/MagicTime/archive/refs/heads/main.zip) to download, or use `git clone --depth=1` command to obtain this repo. * `[2024.04.12]` Thanks [@Jukka Seppänen](https://github.com/kijai) and [@Baobao Wang](https://www.bilibili.com/video/BV1wx421U7Gn/?spm_id_from=333.1007.top_right_bar_window_history.content.click) for providing ComfyUI Extension [ComfyUI-MagicTimeWrapper](https://github.com/kijai/ComfyUI-MagicTimeWrapper). If you find related work, please let us know. * `[2024.04.11]` 🔥 We release the Hugging Face Space of MagicTime, you can click [here](https://huggingface.co/spaces/BestWishYsh/MagicTime?logs=build) to have a try. * `[2024.04.10]` 🔥 We release the inference code and model weight of MagicTime. * `[2024.04.09]` 🔥 We release the arXiv paper for MagicTime, and you can click [here](https://arxiv.org/abs/2404.05014) to see more details. * `[2024.04.08]` 🔥 We release the subset of ChronoMagic dataset used to train MagicTime. The dataset includes 2,265 metamorphic video-text pairs and can be downloaded at [HuggingFace Dataset](https://huggingface.co/datasets/BestWishYsh/ChronoMagic) or [Google Drive](https://drive.google.com/drive/folders/1WsomdkmSp3ql3ImcNsmzFuSQ9Qukuyr8?usp=sharing). * `[2024.04.08]` 🔥 **All codes & datasets** are coming soon! Stay tuned 👀! ## 😮 Highlights MagicTime shows excellent performance in **metamorphic video generation**. ### Metamorphic Videos vs. General Videos Compared to general videos, metamorphic videos contain physical knowledge, long persistence, and strong variation, making them difficult to generate. We show compressed .gif on github, which loses some quality. The general videos are generated by the [Animatediff](https://github.com/guoyww/AnimateDiff) and **MagicTime**.
Type
"Bean sprouts grow and mature from seeds"
"[...] construction in a Minecraft virtual environment"
"Cupcakes baking in an oven [...]"
"[...] transitioning from a tightly closed bud to a fully bloomed state [...]"
General Videos MakeLongVideo MakeLongVideo MakeLongVideo MakeLongVideo
Metamorphic Videos ModelScopeT2V ModelScopeT2V ModelScopeT2V ModelScopeT2V
### Gallery We showcase some metamorphic videos generated by **MagicTime**, [MakeLongVideo](https://github.com/xuduo35/MakeLongVideo), [ModelScopeT2V](https://github.com/modelscope), [VideoCrafter](https://github.com/AILab-CVC/VideoCrafter?tab=readme-ov-file), [ZeroScope](https://huggingface.co/cerspense/zeroscope_v2_576w), [LaVie](https://github.com/Vchitect/LaVie), [T2V-Zero](https://github.com/Picsart-AI-Research/Text2Video-Zero), [Latte](https://github.com/Vchitect/Latte) and [Animatediff](https://github.com/guoyww/AnimateDiff) below.
Method
"cherry blossoms transitioning [...]"
"dough balls baking process [...]"
"an ice cube is melting [...]"
"a simple modern house's construction [...]"
MakeLongVideo MakeLongVideo MakeLongVideo MakeLongVideo MakeLongVideo
ModelScopeT2V ModelScopeT2V ModelScopeT2V ModelScopeT2V ModelScopeT2V
VideoCrafter VideoCrafter VideoCrafter VideoCrafter VideoCrafter
ZeroScope ZeroScope ZeroScope ZeroScope ZeroScope
LaVie LaVie LaVie LaVie LaVie
T2V-Zero T2V-Zero T2V-Zero T2V-Zero T2V-Zero
Latte Latte Latte Latte Latte
Animatediff Animatediff Animatediff Animatediff Animatediff
Ours Ours Ours Ours Ours
We show more metamorphic videos generated by **MagicTime** with the help of [Realistic](https://civitai.com/models/4201/realistic-vision-v20), [ToonYou](https://civitai.com/models/30240/toonyou) and [RcnzCartoon](https://civitai.com/models/66347/rcnz-cartoon-3d).
Realistic Realistic Realistic
"[...] bean sprouts grow and mature from seeds"
"dough [...] swells and browns in the oven [...]"
"the construction [...] in Minecraft [...]"
RcnzCartoon RcnzCartoon RcnzCartoon
"a bud transforms into a yellow flower"
"time-lapse of a plant germinating [...]"
"[...] a modern house being constructed in Minecraft [...]"
ToonYou ToonYou ToonYou
"an ice cube is melting"
"bean plant sprouts grow and mature from the soil"
"time-lapse of delicate pink plum blossoms [...]"
Prompts are trimmed for display, see [here](https://github.com/PKU-YuanGroup/MagicTime/blob/main/__assets__/promtp_unet.txt) for full prompts. ### Integrate into DiT-based Architecture The mission of this project is to help reproduce Sora and provide high-quality video-text data and data annotation pipelines, to support [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan) or other DiT-based T2V models. To this end, we take an initial step to integrate our MagicTime scheme into the DiT-based Framework. Specifically, our method supports the Open-Sora-Plan v1.0.0 for fine-tuning. We first scale up with additional metamorphic landscape time-lapse videos in the same annotation framework to get the ChronoMagic-Landscape dataset. Then, we fine-tune the Open-Sora-Plan v1.0.0 with the ChronoMagic-Landscape dataset to get the MagicTime-DiT model. The results are as follows (**257×512×512 (10s)**):
OpenSora OpenSora OpenSora OpenSora
"Time-lapse of a coastal landscape [...]"
"Display the serene beauty of twilight [...]"
"Sunrise Splendor: Capture the breathtaking moment [...]"
"Nightfall Elegance: Embrace the tranquil beauty [...]"
OpenSora OpenSora OpenSora OpenSora
"The sun descending below the horizon [...]"
"[...] daylight fades into the embrace of the night [...]"
"Time-lapse of the dynamic formations of clouds [...]"
"Capture the dynamic formations of clouds [...]"
Prompts are trimmed for display, see [here](https://github.com/PKU-YuanGroup/MagicTime/blob/main/__assets__/promtp_opensora.txt) for full prompts. ## 🤗 Demo ### Gradio Web UI Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by MagicTime. We also provide [online demo](https://huggingface.co/spaces/BestWishYsh/MagicTime?logs=build) in Hugging Face Spaces. ```bash python app.py ``` ### CLI Inference ```bash # For Realistic python inference_magictime.py --config sample_configs/RealisticVision.yaml --human # or you can directly run the .sh sh inference_cli.sh ``` warning: It is worth noting that even if we use the same seed and prompt but we change a machine, the results will be different. ## ⚙️ Requirements and Installation We recommend the requirements as follows. ### Environment ```bash git clone --depth=1 https://github.com/PKU-YuanGroup/MagicTime.git cd MagicTime conda create -n magictime python=3.10.13 conda activate magictime pip install -r requirements.txt ``` ### Download Base Model and Dreambooth ```bash sh prepare_weights/down_base_model.sh sh prepare_weights/down_dreambooth.sh ``` ### Prepare MagicTime Module ```bash sh prepare_weights/down_magictime_module.sh ``` ## 🗝️ Training & Inference The training code is coming soon! For inference, some examples are shown below: ```bash # For Realistic python inference_magictime.py --config sample_configs/RealisticVision.yaml # For ToonYou python inference_magictime.py --config sample_configs/ToonYou.yaml # For RcnzCartoon python inference_magictime.py --config sample_configs/RcnzCartoon.yaml # or you can directly run the .sh sh inference.sh ``` You can also put all your *custom prompts* in a .txt file and run: ```bash # For Realistic python inference_magictime.py --config sample_configs/RealisticVision.yaml --run-txt XXX.txt --batch-size 2 # For ToonYou python inference_magictime.py --config sample_configs/ToonYou.yaml --run-txt XXX.txt --batch-size 2 # For RcnzCartoon python inference_magictime.py --config sample_configs/RcnzCartoon.yaml --run-txt XXX.txt --batch-size 2 ``` ## Community Contributions We found some plugins created by community developers. Thanks for their efforts: - ComfyUI Extension. [ComfyUI-MagicTimeWrapper](https://github.com/kijai/ComfyUI-MagicTimeWrapper) (by [@Jukka Seppänen](https://github.com/kijai)). And you can click [here](https://www.bilibili.com/video/BV1wx421U7Gn/?spm_id_from=333.1007.top_right_bar_window_history.content.click) to view the installation tutorial. - Replicate Demo & Cloud API. [Replicate-MagicTime](https://replicate.com/camenduru/magictime) (by [@camenduru](https://twitter.com/camenduru)). - Jupyter Notebook. [Jupyter-MagicTime](https://github.com/camenduru/MagicTime-jupyter) (by [@ModelsLab](https://modelslab.com/)). - Diffusers. We need your help to integrate MagicTime into [Diffusers](https://huggingface.co/docs/diffusers). 🙏 **[Need your contribution]** If you find related work, please let us know. ## 🐳 ChronoMagic Dataset ChronoMagic with 2265 metamorphic time-lapse videos, each accompanied by a detailed caption. We released the subset of ChronoMagic used to train MagicTime. The dataset can be downloaded at [HuggingFace Dataset](https://huggingface.co/datasets/BestWishYsh/ChronoMagic), or you can download it with the following command. Some samples can be found on our [Project Page](https://pku-yuangroup.github.io/MagicTime/). ```bash huggingface-cli download --repo-type dataset \ --resume-download BestWishYsh/ChronoMagic \ --local-dir BestWishYsh/ChronoMagic \ --local-dir-use-symlinks False ``` ## 👍 Acknowledgement * [Animatediff](https://github.com/guoyww/AnimateDiff/tree/main) The codebase we built upon and it is a strong U-Net-based text-to-video generation model. * [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan) The codebase we built upon and it is a simple and scalable DiT-based text-to-video generation repo, to reproduce [Sora](https://openai.com/sora). ## 🔒 License * The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/MagicTime/blob/main/LICENSE) file. * The service is a research preview. Please contact us if you find any potential violations. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @article{yuan2024magictime, title={MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators}, author={Yuan, Shenghai and Huang, Jinfa and Shi, Yujun and Xu, Yongqi and Zhu, Ruijie and Lin, Bin and Cheng, Xinhua and Yuan, Li and Luo, Jiebo}, journal={arXiv preprint arXiv:2404.05014}, year={2024} } ``` ## 🤝 Contributors