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MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators

Home Page: https://pku-yuangroup.github.io/MagicTime/

License: Apache License 2.0

Python 99.34% Shell 0.66%
text-to-video video-generation diffusion-models time-lapse time-lapse-dataset open-sora-plan long-video-generation metamorphic-video-generation

magictime's Introduction

If you like our project, please give us a star ⭐ on GitHub for the latest update.

hf_space Replicate demo and cloud API Open In Colab hf_space arXiv Home Page Dataset zhihu zhihu DOI License GitHub Repo stars

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 project that may interest you ✨.

Open-Sora-Plan
PKU-Yuan Lab and Tuzhan AI etc.
github github

ChronoMagic-Bench
Shenghai Yuan, Jinfa Huang and Yujun Shi etc.
github github

📣 News

  • ⏳⏳⏳ Training a stronger model with the support of Open-Sora Plan.
  • ⏳⏳⏳ Release the training code of MagicTime.
  • ⏳⏳⏳ Integrate MagicTime into Diffusers. 🙏 [Need your contribution]
  • [2024.07.29] We add batch inference to inference_magictime.py for easier usage.
  • [2024.06.27] Excited to share our latest ChronoMagic-Bench, a benchmark for metamorphic evaluation of text-to-time-lapse video generation, and is fully open source! Please check out the paper.
  • [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.
  • [2024.04.14] Thanks @camenduru and @ModelsLab for providing Jupyter Notebook and Replicate Demo.
  • [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 to download, or use git clone --depth=1 command to obtain this repo.
  • [2024.04.12] Thanks @Jukka Seppänen and @Baobao Wang for providing ComfyUI Extension 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 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 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 or Google Drive.
  • [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 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, ModelScopeT2V, VideoCrafter, ZeroScope, LaVie, T2V-Zero, Latte and 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, ToonYou and RcnzCartoon.

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 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 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 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 in Hugging Face Spaces.

python app.py

CLI Inference

# 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

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

sh prepare_weights/down_base_model.sh
sh prepare_weights/down_dreambooth.sh

Prepare MagicTime Module

sh prepare_weights/down_magictime_module.sh

🗝️ Training & Inference

The training code is coming soon!

For inference, some examples are shown below:

# 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:

# 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:

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, or you can download it with the following command. Some samples can be found on our Project Page.

huggingface-cli download --repo-type dataset \
--resume-download BestWishYsh/ChronoMagic \
--local-dir BestWishYsh/ChronoMagic \
--local-dir-use-symlinks False

👍 Acknowledgement

  • Animatediff The codebase we built upon and it is a strong U-Net-based text-to-video generation model.

  • 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.

🔒 License

  • The majority of this project is released under the Apache 2.0 license as found in the 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 ⭐ and citation 📝.

@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

magictime's People

Contributors

cheliosoops avatar eltociear avatar fenglui avatar infaaa avatar linb203 avatar shyuanbest avatar truedat101 avatar

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magictime's Issues

License

Hi,
Thanks for releasing this amazing project!
MagicTime is licensed under Apache 2.0, but it says "The service is a research preview intended for non-commercial use only. Please contact us if you find any potential violations."
Apache 2.0 is an open source license, which inherently allows commercial use. But this statement seems to conflict the license.
Would you mind clarifying?
Thank you!

Solving environment: failed, after run conda env create -f environment.yml

Hi,

When I run the command: conda env create -f environment.yml
I got the error below, how to solve it? Thank you.

(base) D:\AI\MagicTime>conda env create -f environment.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound:

  • mkl_random==1.2.4=py310hdb19cb5_0
  • brotli-python==1.0.9=py310h6a678d5_7
  • lerc==3.0=h295c915_0
  • ncurses==6.4=h6a678d5_0
  • pip==23.3.1=py310h06a4308_0
  • mkl==2023.1.0=h213fc3f_46344
  • libtiff==4.5.1=h6a678d5_0
  • typing_extensions==4.9.0=py310h06a4308_1
  • freetype==2.12.1=h4a9f257_0
  • bzip2==1.0.8=h5eee18b_5
  • mkl-service==2.4.0=py310h5eee18b_1
  • zstd==1.5.5=hc292b87_0
  • libwebp-base==1.3.2=h5eee18b_0
  • lcms2==2.12=h3be6417_0
  • nettle==3.7.3=hbbd107a_1
  • python==3.10.13=h955ad1f_0
  • libpng==1.6.39=h5eee18b_0
  • lame==3.100=h7b6447c_0
  • setuptools==68.2.2=py310h06a4308_0
  • openjpeg==2.4.0=h3ad879b_0
  • pillow==10.2.0=py310h5eee18b_0
  • zlib==1.2.13=h5eee18b_0
  • gmp==6.2.1=h295c915_3
  • libcufft==10.7.2.124=h4fbf590_0
  • urllib3==2.1.0=py310h06a4308_1
  • idna==3.4=py310h06a4308_0
  • libidn2==2.3.4=h5eee18b_0
  • libunistring==0.9.10=h27cfd23_0
  • requests==2.31.0=py310h06a4308_1
  • libuuid==1.41.5=h5eee18b_0
  • wheel==0.41.2=py310h06a4308_0
  • _openmp_mutex==5.1=1_gnu
  • ca-certificates==2024.3.11=h06a4308_0
  • readline==8.2=h5eee18b_0
  • intel-openmp==2023.1.0=hdb19cb5_46306
  • gnutls==3.6.15=he1e5248_0
  • mkl_fft==1.3.8=py310h5eee18b_0
  • ld_impl_linux-64==2.38=h1181459_1
  • libdeflate==1.17=h5eee18b_1
  • numpy-base==1.26.4=py310hb5e798b_0
  • libffi==3.4.4=h6a678d5_0
  • libgomp==11.2.0=h1234567_1
  • openh264==2.1.1=h4ff587b_0
  • sqlite==3.41.2=h5eee18b_0
  • pytorch-cuda==11.7=h778d358_5
  • pytorch==1.13.1=py3.10_cuda11.7_cudnn8.5.0_0
  • pysocks==1.7.1=py310h06a4308_0
  • libcufile==1.9.0.20=0
  • libtasn1==4.19.0=h5eee18b_0
  • openssl==3.0.13=h7f8727e_0
  • ffmpeg==4.3=hf484d3e_0
  • tk==8.6.12=h1ccaba5_0
  • tbb==2021.8.0=hdb19cb5_0
  • numpy==1.26.4=py310h5f9d8c6_0
  • jpeg==9e=h5eee18b_1
  • libiconv==1.16=h7f8727e_2
  • libgcc-ng==11.2.0=h1234567_1
  • xz==5.4.6=h5eee18b_0
  • lz4-c==1.9.4=h6a678d5_0
  • libstdcxx-ng==11.2.0=h1234567_1
  • certifi==2024.2.2=py310h06a4308_0

torch.cuda.OutOfMemoryError: CUDA out of memory.

Running inference on ubutnu 22.04 with an NVIDIA 3080 (12GB), getting:

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.25 GiB. GPU 0 has a total capacity of 11.75 GiB of which 1.08 GiB is free. Including non-PyTorch memory, this process has 9.95 GiB memory in use. Of the allocated memory 9.51 GiB is allocated by PyTorch, and 139.25 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

Looking for ways to tune configuration so OOM does not happen.

shell scripts in prepare_weights don't work without modification

Probably there is a difference in the current developer "internal" build vs what got released to OSS. The issues are identified below with solution:

  • git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 ckpts/Base_Model will fail because Base_Model exists. It really should be: git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 ckpts/Base_Model/stable-diffusion-v1-5
  • Without previous change, later when running inference scripts, it will fail because stable-diffusion-v1-5 path is not found. fix is in previous step
  • When re-running these scripts, they will fail because directories exist. TODO: identify a way to "update" ... possibly add update scripts if the models will change.
  • in the prepare_weights/down_magictime_module.sh, you cannot move the repository without first deleting the .git subdirectory inside of MagicTime/.git because the clone repository has a write protected object. Why not use a git submodule? Or simply remove the .git dir in the freshly cloned subdirectory.
  • The mv dir command in prepare_weights/down_magictime_module.sh because the directory exists. Either use cp -r, or simply do mv MagicTime/Magic_Weights/* ckpts/Magic_Weights/

Exception running inference_magictime.py _pickle.UnpicklingError: invalid load key, 'v'.

Pickle error on Linux ubuntu 22.04.

Will investigate.

10:42 $ python inference_magictime.py --config sample_configs/RealisticVision.yaml
The results will be save to outputs/RealisticVision-7
Use MagicAdapter-S
Use MagicAdapter-T
Use Magic_Text_Encoder
loaded 3D unet's pretrained weights from ./ckpts/Base_Model/stable-diffusion-v1-5/unet ...
### missing keys: 560; 
### unexpected keys: 0;
### Motion Module Parameters: 417.1376 M
load motion module from ./ckpts/Base_Model/motion_module/motion_module.ckpt
load dreambooth model from ./ckpts/DreamBooth/RealisticVisionV60B1_v51VAE.safetensors
load domain lora from ./ckpts/Magic_Weights/magic_adapter_s/magic_adapter_s.ckpt
Traceback (most recent call last):
  File "MagicTime/inference_magictime.py", line 249, in <module>
    main(args)
  File "miniforge3/envs/magictime/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "MagicTime/inference_magictime.py", line 76, in main
    pipeline = load_weights(
  File "MagicTime/utils/util.py", line 137, in load_weights
    magic_adapter_s_state_dict = torch.load(magic_adapter_s_path, map_location="cpu")
  File "miniforge3/envs/magictime/lib/python3.10/site-packages/torch/serialization.py", line 1040, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "miniforge3/envs/magictime/lib/python3.10/site-packages/torch/serialization.py", line 1258, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, 'v'.

safetensors_rust.SafetensorError: Error while deserializing header: HeaderTooLarge

i install everything correctly and when i want to run an inference , i get this error.
People on the net with the same error told me that it can be corrupted file so i download everything again ( multiple time and i keep getting this error) here the full error message

Traceback (most recent call last):
File "/home/azureuser/localfiles/MagicTime/inference_magictime.py", line 249, in
main(args)
File "/anaconda/envs/magictime/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/azureuser/localfiles/MagicTime/inference_magictime.py", line 61, in main
text_encoder = CLIPTextModel.from_pretrained(model_config.pretrained_model_path, subfolder="text_encoder").cuda()
File "/anaconda/envs/magictime/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3284, in from_pretrained
with safe_open(resolved_archive_file, framework="pt") as f:
safetensors_rust.SafetensorError: Error while deserializing header: HeaderTooLarge

Exception: Error while deserializing header: HeaderTooLarge

Header to large error when running example. I suspect this is a problem with how I downloaded the models when running prep scripts. Will retry from scratch.

11:32 $ python inference_magictime.py --config sample_configs/RealisticVision.yaml
The results will be save to outputs/RealisticVision-3
Use MagicAdapter-S
Use MagicAdapter-T
Use Magic_Text_Encoder
Traceback (most recent call last):
  File "myhome/dev/repos/MagicTime/inference_magictime.py", line 249, in <module>
    main(args)
  File "myhome/tools/miniforge3/envs/magictime/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/home/dkords/dev/repos/MagicTime/inference_magictime.py", line 61, in main
    text_encoder = CLIPTextModel.from_pretrained(model_config.pretrained_model_path, subfolder="text_encoder").cuda()
  File "myhome//tools/miniforge3/envs/magictime/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3284, in from_pretrained
    with safe_open(resolved_archive_file, framework="pt") as f:
safetensors_rust.SafetensorError: Error while deserializing header: HeaderTooLarge

how to get videos longer than 2 sec?

Also what does the video_length=16 imply? Is that number of frames? Tried changing it to 32 but it just seems to generate blank videos. Example:

saample.mp4

magictime_model_loader:

Error occurred when executing magictime_model_loader:

No module named 'swift'

what does it means ?

Attempt to open cnn_infer failed: handle=0 error: libcudnn_cnn_infer.so.8: cannot open shared object file: No such file or directory

Triggered "warning" during inference run on Ubuntu 22.04. Considering this issue as a warning unless someone says the warning should be treated as an error. Possibly there are some extra dev / optional modules not installed.

miniforge3/envs/magictime/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Attempt to open cnn_infer failed: handle=0 error: libcudnn_cnn_infer.so.8: cannot open shared object file: No such file or directory (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:78.)

[BUG]刚部署好,但是运行有BUG

(magictime) root@autodl-container-e3fa488242-5bb059c5:~/autodl-tmp/MagicTime# python app.py --port 6006

Cleaning cached examples ...

loaded 3D unet's pretrained weights from ./ckpts/Base_Model/stable-diffusion-v1-5/unet ...

missing keys: 560;

unexpected keys: 0;

Motion Module Parameters: 417.1376 M

loaded 3D unet's pretrained weights from ./ckpts/Base_Model/stable-diffusion-v1-5/unet ...

missing keys: 560;

unexpected keys: 0;

Motion Module Parameters: 417.1376 M

2024-04-15 13:52:45,055 - modelscope - INFO - PyTorch version 2.2.2 Found.
2024-04-15 13:52:45,055 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer
2024-04-15 13:52:45,087 - modelscope - INFO - Loading done! Current index file version is 1.13.3, with md5 1d4b83741562033e1de185bbe18433db and a total number of 972 components indexed
Traceback (most recent call last):
File "/root/autodl-tmp/MagicTime/app.py", line 235, in
controller = MagicTimeController()
File "/root/autodl-tmp/MagicTime/app.py", line 105, in init
self.update_dreambooth(self.dreambooth_list[0])
File "/root/autodl-tmp/MagicTime/app.py", line 149, in update_dreambooth
magic_adapter_s_state_dict = torch.load(magic_adapter_s_path, map_location="cpu")
File "/root/miniconda3/envs/magictime/lib/python3.10/site-packages/torch/serialization.py", line 1040, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/root/miniconda3/envs/magictime/lib/python3.10/site-packages/torch/serialization.py", line 1258, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, 'v'.

app.py的运行完全是按照官网教程部署的。所有缺失的模型都已经安装,但是还是出现了这个错误,麻烦解决一下子。

Windows not supported?

    from multiprocessing.context import ForkProcess
ImportError: cannot import name 'ForkProcess' from 'multiprocessing.context' (D:\Python\lib\multiprocessing\context.py)

ForkProcess only on Unix? Any chance you can patch it for Windows compatibility too?

confusing about the Cascade Preprocessing

Hi authors, thank you for your nice work!

I am confused about the Cascade Preprocessing present in the paper. Could you explain more about the motivations as well as the definition of transiation point? Thanks.

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