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<div align="center"> <img src="sat/assets/icon/icon.jpg" width="250"/> <h2><center>Tora: Trajectory-oriented Diffusion Transformer for Video Generation</h2>Zhenghao Zhang*, Junchao Liao*, Menghao Li, Zuozhuo Dai, Bingxue Qiu, Siyu Zhu, Long Qin, Weizhi Wang
* equal contribution
<a href='https://arxiv.org/abs/2407.21705'><img src='https://img.shields.io/badge/ArXiv-2407.21705-red'></a> <a href='https://ali-videoai.github.io/tora_video/'><img src='https://img.shields.io/badge/Project-Page-Blue'></a> <a href="https://github.com/alibaba/Tora"><img src='https://img.shields.io/badge/Github-Link-orange'></a> <a href='https://www.modelscope.cn/studios/xiaoche/Tora'><img src='https://img.shields.io/badge/π€_ModelScope-ZH_demo-%23654dfc'></a> <a href='https://www.modelscope.cn/studios/Alibaba_Research_Intelligence_Computing/Tora_En'><img src='https://img.shields.io/badge/π€_ModelScope-EN_demo-%23654dfc'></a>
<a href='https://modelscope.cn/models/xiaoche/Tora'><img src='https://img.shields.io/badge/π€_ModelScope-weights-%23654dfc'></a> <a href='https://huggingface.co/Le0jc/Tora'><img src='https://img.shields.io/badge/π€_HuggingFace-weights-%23ff9e0e'></a>
</div>This is the official repository for paper "Tora: Trajectory-oriented Diffusion Transformer for Video Generation".
π‘ Abstract
Recent advancements in Diffusion Transformer (DiT) have demonstrated remarkable proficiency in producing high-quality video content. Nonetheless, the potential of transformer-based diffusion models for effectively generating videos with controllable motion remains an area of limited exploration. This paper introduces Tora, the first trajectory-oriented DiT framework that integrates textual, visual, and trajectory conditions concurrently for video generation. Specifically, Tora consists of a Trajectory Extractor (TE), a Spatial-Temporal DiT, and a Motion-guidance Fuser (MGF). The TE encodes arbitrary trajectories into hierarchical spacetime motion patches with a 3D video compression network. The MGF integrates the motion patches into the DiT blocks to generate consistent videos following trajectories. Our design aligns seamlessly with DiTβs scalability, allowing precise control of video contentβs dynamics with diverse durations, aspect ratios, and resolutions. Extensive experiments demonstrate Toraβs excellence in achieving high motion fidelity, while also meticulously simulating the movement of physical world.
π£ Updates
2024/10/31
Model weights uploaded to HuggingFace. We also provided an English demo on ModelScope.2024/10/23
π₯π₯Our ModelScope Demo is launched. Welcome to try it out! We also upload the model weights to ModelScope.2024/10/21
Thanks to @kijai for supporting Tora in ComfyUI! Link2024/10/15
π₯π₯We released our inference code and model weights. Please note that this is a CogVideoX version of Tora, built on the CogVideoX-5B model. This version of Tora is meant for academic research purposes only. Due to our commercial plans, we will not be open-sourcing the complete version of Tora at this time.2024/08/27
We released our v2 paper including appendix.2024/07/31
We submitted our paper on arXiv and released our project page.
π Table of Contents
- Showcases
- TODO List
- Installation
- Model Weights
- Inference
- Gradio Demo
- Troubleshooting
- Acknowledgements
- Our previous work
- Citation
ποΈ Showcases
https://github.com/user-attachments/assets/949d5e99-18c9-49d6-b669-9003ccd44bf1
https://github.com/user-attachments/assets/7e7dbe87-a8ba-4710-afd0-9ef528ec329b
https://github.com/user-attachments/assets/4026c23d-229d-45d7-b5be-6f3eb9e4fd50
All videos are available in this Link
β TODO List
- Release our inference code and model weights
- Provide a ModelScope Demo
- Release our training code
- Release complete version of Tora
π Installation
# Clone this repository.
git clone https://github.com/alibaba/Tora.git
cd Tora
# Install Pytorch (we use Pytorch 2.4.0) and torchvision following the official instructions: https://pytorch.org/get-started/previous-versions/. For example:
conda create -n tora python==3.10
conda activate tora
conda install pytorch==2.4.0 torchvision==0.19.0 pytorch-cuda=12.1 -c pytorch -c nvidia
# Install requirements
cd modules/SwissArmyTransformer
pip install -e .
cd ../../sat
pip install -r requirements.txt
cd ..
π¦ Model Weights
Folder Structure
Tora
βββ sat
βββ ckpts
βββ t5-v1_1-xxl
β βββ model-00001-of-00002.safetensors
β βββ ...
βββ vae
β βββ 3d-vae.pt
βββ tora
βββ t2v
βββ mp_rank_00_model_states.pt
Download Links
Note: Downloading the tora
weights requires following the CogVideoX License. You can choose one of the following options: HuggingFace, ModelScope, or native links.
After downloading the model weights, you can put them in the Tora/sat/ckpts
folder.
HuggingFace
# This can be faster
pip install "huggingface_hub[hf_transfer]"
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download Le0jc/Tora --local-dir ckpts
or
# use git
git lfs install
git clone https://huggingface.co/Le0jc/Tora
ModelScope
- SDK
from modelscope import snapshot_download
model_dir = snapshot_download('xiaoche/Tora')
- Git
git clone https://www.modelscope.cn/xiaoche/Tora.git
Native
- Download the VAE and T5 model following CogVideo:
- Tora t2v model weights: Link. Downloading this weight requires following the CogVideoX License.
π Inference
It requires around 30 GiB GPU memory tested on NVIDIA A100.
cd sat
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True torchrun --standalone --nproc_per_node=$N_GPU sample_video.py --base configs/tora/model/cogvideox_5b_tora.yaml configs/tora/inference_sparse.yaml --load ckpts/tora/t2v --output-dir samples --point_path trajs/coaster.txt --input-file assets/text/t2v/examples.txt
You can change the --input-file
and --point_path
to your own prompts and trajectory points files. Please note that the trajectory is drawn on a 256x256 canvas.
Replace $N_GPU
with the number of GPUs you want to use.
Recommendations for Text Prompts
For text prompts, we highly recommend using GPT-4 to enhance the details. Simple prompts may negatively impact both visual quality and motion control effectiveness.
You can refer to the following resources for guidance:
π₯οΈ Gradio Demo
Usage:
cd sat
python app.py --load ckpts/tora/t2v
π― Troubleshooting
1. ValueError: Non-consecutive added token...
Upgrade the transformers package to 4.44.2. See this issue.
π€ Acknowledgements
We would like to express our gratitude to the following open-source projects that have been instrumental in the development of our project:
- CogVideo: An open source video generation framework by THUKEG.
- Open-Sora: An open source video generation framework by HPC-AI Tech.
- MotionCtrl: A video generation model supporting motion control by ARC Lab, Tencent PCG.
- ComfyUI-DragNUWA: An implementation of DragNUWA for ComfyUI.
Special thanks to the contributors of these libraries for their hard work and dedication!
π Our previous work
π Citation
@misc{zhang2024toratrajectoryorienteddiffusiontransformer,
title={Tora: Trajectory-oriented Diffusion Transformer for Video Generation},
author={Zhenghao Zhang and Junchao Liao and Menghao Li and Zuozhuo Dai and Bingxue Qiu and Siyu Zhu and Long Qin and Weizhi Wang},
year={2024},
eprint={2407.21705},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.21705},
}