# Open-Sora-Plan
**Repository Path**: hqsrawmelon/Open-Sora-Plan
## Basic Information
- **Project Name**: Open-Sora-Plan
- **Description**: fork of https://github.com/PKU-YuanGroup/Open-Sora-Plan
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-06
- **Last Updated**: 2024-10-14
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Open-Sora Plan
[[Project Page]](https://pku-yuangroup.github.io/Open-Sora-Plan/) [[中文主页]](https://pku-yuangroup.github.io/Open-Sora-Plan/blog_cn.html)
## Goal
This project aims to create a simple and scalable repo, to reproduce [Sora](https://openai.com/sora) (OpenAI, but we prefer to call it "CloseAI" ) and build knowledge about Video-VQVAE (VideoGPT) + DiT at scale. However, we have limited resources, we deeply wish all open-source community can contribute to this project. Pull request are welcome!!!
本项目希望通过开源社区的力量复现Sora，由北大-兔展AIGC联合实验室共同发起，当前我们资源有限仅搭建了基础架构，无法进行完整训练，希望通过开源社区逐步增加模块并筹集资源进行训练，当前版本离目标差距巨大，仍需持续完善和快速迭代，欢迎Pull request！！！
Project stages:
- Primary
1. Setup the codebase and train a un-conditional model on landscape dataset.
2. Train models that boost resolution and duration.
- Extensions
3. Conduct text2video experiments on landscape dataset.
4. Train the 1080p model on video2text dataset.
5. Control model with more condition.
## News
**[2024.03.05]** See our latest [todo](https://github.com/PKU-YuanGroup/Open-Sora-Plan?tab=readme-ov-file#todo), welcome to pull request.
**[2024.03.04]** We re-organize and modulize our codes and make it easy to [contribute](https://github.com/PKU-YuanGroup/Open-Sora-Plan?tab=readme-ov-file#how-to-contribute-to-the-open-sora-plan-community) to the project, please see the [Repo structure](https://github.com/PKU-YuanGroup/Open-Sora-Plan?tab=readme-ov-file#repo-structure).
**[2024.03.03]** We open some [discussions](https://github.com/PKU-YuanGroup/Open-Sora-Plan/discussions) and clarify several issues.
**[2024.03.01]** Training codes are available now! Learn more in our [project page](https://pku-yuangroup.github.io/Open-Sora-Plan/). Please feel free to watch 👀 this repository for the latest updates.
## Todo
#### Setup the codebase and train a unconditional model on landscape dataset
- [x] Setup repo-structure.
- [x] Add Video-VQGAN model, which is borrowed from [VideoGPT](https://github.com/wilson1yan/VideoGPT).
- [x] Support variable aspect ratios, resolutions, durations training on [DiT](https://github.com/facebookresearch/DiT).
- [x] Support Dynamic mask input inspired [FiT](https://github.com/whlzy/FiT).
- [x] Add class-conditioning on embeddings.
- [ ] Incorporating [Latte](https://github.com/Vchitect/Latte) as main codebase.
- [ ] Add VAE model, which is borrowed from [Stable Diffusion](https://github.com/CompVis/latent-diffusion).
- [ ] Joint dynamic mask input with VAE.
- [ ] Make the codebase ready for the cluster training. Add SLURM scripts.
- [ ] Add sampling script.
- [ ] Incorporating [SiT](https://github.com/willisma/SiT).
#### Train models that boost resolution and duration
- [ ] Add [PI](https://arxiv.org/abs/2306.15595) to support out-of-domain size.
- [x] Add frame interpolation model.
#### Conduct text2video experiments on landscape dataset.
- [ ] Finish data loading, pre-processing utils.
- [ ] Add CLIP and T5 support.
- [ ] Add text2image training script.
- [ ] Add prompt captioner.
#### Train the 1080p model on video2text dataset
- [ ] Looking for a suitable dataset, welcome to discuss and recommend.
- [ ] Finish data loading, pre-processing utils.
- [ ] Support memory friendly training.
- [ ] Add flash-attention2 from pytorch.
- [ ] Add xformers.
- [ ] Add accelerate to automatically manage training, e.g. mixed precision training.
- [ ] Add gradient checkpoint.
- [ ] Train using the deepspeed engine.
#### Control model with more condition
- [ ] Load pretrained weight from [PixArt-α](https://github.com/PixArt-alpha/PixArt-alpha).
- [ ] Incorporating [ControlNet](https://github.com/lllyasviel/ControlNet).
## Repo structure
```
├── README.md
├── docs
│ ├── Data.md -> Datasets description.
│ ├── Contribution_Guidelines.md -> Contribution guidelines description.
├── scripts -> All training scripts.
│ └── train.sh
├── sora
│ ├── dataset -> Dataset code to read videos
│ ├── models
│ │ ├── captioner
│ │ ├── super_resolution
│ ├── modules
│ │ ├── ae -> compress videos to latents
│ │ │ ├── vqvae
│ │ │ ├── vae
│ │ ├── diffusion -> denoise latents
│ │ │ ├── dit
│ │ │ ├── unet
| ├── utils.py
│ ├── train.py -> Training code
```
## Requirements and Installation
The recommended requirements are as follows.
* Python >= 3.8
* Pytorch >= 1.13.1
* CUDA Version >= 11.7
* Install required packages:
```
git clone https://github.com/PKU-YuanGroup/Open-Sora-Plan
cd Open-Sora-Plan
conda create -n opensora python=3.8 -y
conda activate opensora
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
cd src/sora/modules/ae/vqvae/videogpt/
pip install -e .
cd ..
```
## Usage
### Datasets
Refer to [Data.md](docs/Data.md)
### Video-VQVAE (VideoGPT)
#### Training
```
cd src/sora/modules/ae/vqvae/videogpt
```
Refer to origin [repo](https://github.com/wilson1yan/VideoGPT?tab=readme-ov-file#training-vq-vae). Use the `scripts/train_vqvae.py` script to train a Video-VQVAE. Execute `python scripts/train_vqvae.py -h` for information on all available training settings. A subset of more relevant settings are listed below, along with default values.
##### VQ-VAE Specific Settings
* `--embedding_dim`: number of dimensions for codebooks embeddings
* `--n_codes 2048`: number of codes in the codebook
* `--n_hiddens 240`: number of hidden features in the residual blocks
* `--n_res_layers 4`: number of residual blocks
* `--downsample 4 4 4`: T H W downsampling stride of the encoder
##### Training Settings
* `--gpus 2`: number of gpus for distributed training
* `--sync_batchnorm`: uses `SyncBatchNorm` instead of `BatchNorm3d` when using > 1 gpu
* `--gradient_clip_val 1`: gradient clipping threshold for training
* `--batch_size 16`: batch size per gpu
* `--num_workers 8`: number of workers for each DataLoader
##### Dataset Settings
* `--data_path `: path to an `hdf5` file or a folder containing `train` and `test` folders with subdirectories of videos
* `--resolution 128`: spatial resolution to train on
* `--sequence_length 16`: temporal resolution, or video clip length
#### Reconstructing
```Python
python rec_video.py --video-path "assets/origin_video_0.mp4" --rec-path "rec_video_0.mp4" --num-frames 500 --sample-rate 1
```
```Python
python rec_video.py --video-path "assets/origin_video_1.mp4" --rec-path "rec_video_1.mp4" --resolution 196 --num-frames 600 --sample-rate 1
```
We present four reconstructed videos in this demonstration, arranged from left to right as follows:
| **3s 596x336** | **10s 256x256** | **18s 196x196** | **24s 168x96** |
| --- | --- | --- | --- |
| 
| 
| 
| 
|
### VideoDiT (DiT)
#### Training
```
sh scripts/train.sh
```
#### Sampling
Coming soon.
## How to Contribute to the Open-Sora Plan Community
We greatly appreciate your contributions to the Open-Sora Plan open-source community and helping us make it even better than it is now!
For more details, please refer to the [Contribution Guidelines](docs/Contribution_Guidelines.md)
## Acknowledgement
* [DiT](https://github.com/facebookresearch/DiT/tree/main): Scalable Diffusion Models with Transformers.
* [VideoGPT](https://github.com/wilson1yan/VideoGPT): Video Generation using VQ-VAE and Transformers.
* [FiT](https://github.com/whlzy/FiT): Flexible Vision Transformer for Diffusion Model.
* [Positional Interpolation](https://arxiv.org/abs/2306.15595): Extending Context Window of Large Language Models via Positional Interpolation.
## License
* The service is a research preview intended for non-commercial use only. See [LICENSE.txt](LICENSE.txt) for details.
## Contributors
