Open-Sora Plan

We are thrilled to present Open-Sora-Plan v1.0.0, which significantly enhances video generation quality and text control capabilities. See our report. We are training for higher resolution (>1024) as well as longer duration (>10s) videos, here is a preview of the next release. We show compressed .gif on GitHub, which loses some quality.

Thanks to HUAWEI Ascend NPU Team for supporting us.

目前已支持国产AI芯片(华为昇腾910，期待更多国产算力芯片)进行推理，下一步将支持国产算力训练，具体可参考昇腾分支hw branch.

257×512×512 (10s)	65×1024×1024 (2.7s)	65×1024×1024 (2.7s)

Time-lapse of a coastal landscape transitioning from sunrise to nightfall...	A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....	Sunset over the sea.

65×512×512 (2.7s)	65×512×512 (2.7s)	65×512×512 (2.7s)

A serene underwater scene featuring a sea turtle swimming...	Yellow and black tropical fish dart through the sea.	a dynamic interaction between the ocean and a large rock...

The dynamic movement of tall, wispy grasses swaying in the wind...	Slow pan upward of blazing oak fire in an indoor fireplace.	A serene waterfall cascading down moss-covered rocks...

💪 Goal

This project aims to create a simple and scalable repo, to reproduce Sora (OpenAI, but we prefer to call it "ClosedAI" ). We wish the open-source community can contribute to this project. Pull requests 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，当前版本离目标差距仍然较大，仍需持续完善和快速迭代，欢迎Pull request！！！

Project stages:

Primary

Setup the codebase and train an un-conditional model on a landscape dataset.
Train models that boost resolution and duration.

Extensions

Conduct text2video experiments on landscape dataset.
Train the 1080p model on video2text dataset.
Control model with more conditions.

📰 News

[2024.04.09] 🚀 Excited to share our latest exploration on metamorphic time-lapse video generation: MagicTime, which learns real-world physics knowledge from time-lapse videos. Here is the dataset for train (updating): Open-Sora-Dataset.

[2024.04.07] 🔥🔥🔥 Today, we are thrilled to present Open-Sora-Plan v1.0.0, which significantly enhances video generation quality and text control capabilities. See our report. Thanks to HUAWEI NPU for supporting us.

[2024.03.27] 🚀🚀🚀 We release the report of VideoCausalVAE, which supports both images and videos. We present our reconstructed video in this demonstration as follows. The text-to-video model is on the way.

[2024.03.10] 🚀🚀🚀 This repo supports training a latent size of 225×90×90 (t×h×w), which means we are able to train 1 minute of 1080P video with 30FPS (2× interpolated frames and 2× super resolution) under class-condition.

[2024.03.08] We support the training code of text condition with 16 frames of 512x512. The code is mainly borrowed from Latte.

[2024.03.07] We support training with 128 frames (when sample rate = 3, which is about 13 seconds) of 256x256, or 64 frames (which is about 6 seconds) of 512x512.

[2024.03.05] See our latest todo, pull requests are welcome.

[2024.03.04] We re-organize and modulize our code to make it easy to contribute to the project, to contribute please see the Repo structure.

[2024.03.03] We open some discussions to clarify several issues.

[2024.03.01] Training code is available now! Learn more on our project page. Please feel free to watch 👀 this repository for the latest updates.

✊ Todo

Setup the codebase and train an unconditional model on landscape dataset

Train models that boost resolution and duration

Conduct text2video experiments on landscape dataset.

Train the 1080p model on video2text dataset

Control model with more condition

Incorporating ControlNet. ⌛ [WIP] 🙏 [Need your contribution]

📂 Repo structure (WIP)

├── README.md
├── docs
│   ├── Data.md                    -> Datasets description.
│   ├── Contribution_Guidelines.md -> Contribution guidelines description.
├── scripts                        -> All scripts.
├── opensora
│   ├── dataset
│   ├── models
│   │   ├── ae                     -> Compress videos to latents
│   │   │   ├── imagebase
│   │   │   │   ├── vae
│   │   │   │   └── vqvae
│   │   │   └── videobase
│   │   │       ├── vae
│   │   │       └── vqvae
│   │   ├── captioner
│   │   ├── diffusion              -> Denoise latents
│   │   │   ├── diffusion         
│   │   │   ├── dit
│   │   │   ├── latte
│   │   │   └── unet
│   │   ├── frame_interpolation
│   │   ├── super_resolution
│   │   └── text_encoder
│   ├── sample
│   ├── train                      -> Training code
│   └── utils

🛠️ Requirements and Installation

Clone this repository and navigate to Open-Sora-Plan folder

git clone https://github.com/PKU-YuanGroup/Open-Sora-Plan
cd Open-Sora-Plan

Install required packages

conda create -n opensora python=3.8 -y
conda activate opensora
pip install -e .

Install additional packages for training cases

pip install -e ".[train]"
pip install flash-attn --no-build-isolation

Install optional requirements such as static type checking:

pip install -e '.[dev]'

🗝️ Usage

🤗 Demo

Gradio Web UI

Highly recommend trying out our web demo by the following command. We also provide online demo and in Huggingface Spaces.

🤝 Enjoying the and , created by @camenduru, who generously supports our research!

python -m opensora.serve.gradio_web_server

CLI Inference

sh scripts/text_condition/sample_video.sh

Datasets

Refer to Data.md

Evaluation

Refer to the document EVAL.md.

Causal Video VAE

Reconstructing

python examples/rec_video_vae.py --rec-path test_video.mp4 --video-path video.mp4 --resolution 512 --num-frames 1440 --sample-rate 1 --sample-fps 24 -
-device cuda --ckpt <Your ckpt>

Training and Inference

Please refer to the document CausalVideoVAE.

VideoGPT VQVAE

Please refer to the document VQVAE.

Video Diffusion Transformer

Training

sh scripts/text_condition/train_videoae_17x256x256.sh

sh scripts/text_condition/train_videoae_65x256x256.sh

sh scripts/text_condition/train_videoae_65x512x512.sh

🚀 Improved Training Performance

In comparison to the original implementation, we implement a selection of training speed acceleration and memory saving features including gradient checkpointing, mixed precision training, and pre-extracted features, xformers, deepspeed. Some data points using a batch size of 1 with a A100:

64×32×32 (origin size: 256×256×256)

gradient checkpointing	mixed precision	xformers	feature pre-extraction	deepspeed config	compress kv	training speed	memory
✔	✔	✔	✔	❌	❌	0.64 steps/sec	43G
✔	✔	✔	✔	Zero2	❌	0.66 steps/sec	14G
✔	✔	✔	✔	Zero2	✔	0.66 steps/sec	15G
✔	✔	✔	✔	Zero2 offload	❌	0.33 steps/sec	11G
✔	✔	✔	✔	Zero2 offload	✔	0.31 steps/sec	12G

128×64×64 (origin size: 512×512×512)

gradient checkpointing	mixed precision	xformers	feature pre-extraction	deepspeed config	compress kv	training speed	memory
✔	✔	✔	✔	❌	❌	0.08 steps/sec	77G
✔	✔	✔	✔	Zero2	❌	0.08 steps/sec	41G
✔	✔	✔	✔	Zero2	✔	0.09 steps/sec	36G
✔	✔	✔	✔	Zero2 offload	❌	0.07 steps/sec	39G
✔	✔	✔	✔	Zero2 offload	✔	0.07 steps/sec	33G

💡 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

👍 Acknowledgement

Latte: The main codebase we built upon and it is an wonderful video generated model.
PixArt-alpha: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.
VideoGPT: Video Generation using VQ-VAE and Transformers.
DiT: Scalable Diffusion Models with Transformers.
FiT: Flexible Vision Transformer for Diffusion Model.
Positional Interpolation: Extending Context Window of Large Language Models via Positional Interpolation.

🔒 License

See LICENSE for details.

✏️ Citing

BibTeX

@software{pku_yuan_lab_and_tuzhan_ai_etc_2024_10948109,
  author       = {PKU-Yuan Lab and Tuzhan AI etc.},
  title        = {Open-Sora-Plan},
  month        = apr,
  year         = 2024,
  publisher    = {GitHub},
  doi          = {10.5281/zenodo.10948109},
  url          = {https://doi.org/10.5281/zenodo.10948109}
}

InsideLifej/Open-Sora-Plan