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PKU-YuanGroup/Open-Sora-Plan

Fork: 931 Star: 10363 (更新于 2024-05-27 19:11:49)

license: MIT

Language: Python .

This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.

最后发布版本: v1.1.0 ( 2024-05-27 18:02:22)

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Open-Sora Plan

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v1.0.0 badge [![Twitter](https://img.shields.io/badge/-Twitter@LinBin46984-black?logo=twitter&logoColor=1D9BF0)](https://x.com/LinBin46984/status/1763476690385424554?s=20)
[![hf_space](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/LanguageBind/Open-Sora-Plan-v1.0.0) [![hf_space](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/fffiloni/Open-Sora-Plan-v1-0-0) [![Replicate demo and cloud API](https://replicate.com/camenduru/open-sora-plan-512x512/badge)](https://replicate.com/camenduru/open-sora-plan-512x512) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/Open-Sora-Plan-jupyter/blob/main/Open_Sora_Plan_jupyter.ipynb)

We are thrilled to present Open-Sora-Plan v1.1.0, which significantly enhances video generation quality and text control capabilities. See our report. We show compressed .gif on GitHub, which loses some quality.

Thanks to HUAWEI Ascend Team for supporting us. In the second stage, we used Huawei Ascend computing power for training. This stage's training and inference were fully supported by Huawei. Models trained on Huawei Ascend can also be loaded into GPUs and generate videos of the same quality.

目前已经支持使用国产AI芯片(华为昇腾,期待更多国产算力芯片)进行完整的训练和推理。在项目第二阶段,所有训练和推理任务完全由华为昇腾芯片支持。此外,基于华为昇腾的512卡集群训练出的模型,也可以无缝地在GPU上运行,并保持相同的视频质量。详细信息请参考我们的hw branch.

221×512×512 Text-to-Video Generation

221×512×512 (9.2s) 221×512×512 (9.2s) 221×512×512 (9.2s)
3D animation of a small, round, fluffy creature with big, expressive eyes explores ... A single drop of liquid metal falls from a floating orb, landing on a mirror-like ... The video presents an abstract composition centered around a hexagonal shape adorned ...
A drone camera circles around a beautiful historic church built on a rocky outcropping ... Aerial view of Santorini during the blue hour, showcasing the stunning architecture ... An aerial shot of a lighthouse standing tall on a rocky cliff, its beacon cutting ...
A snowy forest landscape with a dirt road running through it. The road is flanked by ... Drone shot along the Hawaii jungle coastline, sunny day. Kayaks in the water. The camera rotates around a large stack of vintage televisions all showing different ...

65×512×512 Text-to-Video Generation

65×512×512 (2.7s) 65×512×512 (2.7s) 65×512×512 (2.7s)
In an ornate, historical hall, a massive tidal wave peaks and begins to crash. Two ... A Shiba Inu dog wearing a beret and black turtleneck. A painting of a boat on water comes to life, with waves crashing and the boat becoming ...
A person clad in a space suit with a helmet and equipped with a chest light and arm ... 3D animation of a small, round, fluffy creature with big, expressive eyes explores a ... In a studio, there is a painting depicting a ship sailing through the rough sea.
A robot dog trots down a deserted alley at night, its metallic paws clinking softly ... A lone surfer rides a massive wave, skillfully maneuvering through the surf. The water ... A solitary cheetah sprints across the savannah, its powerful muscles propelling it ...

65×512×512 Video Editing

generated 65×512×512 (2.7s) edited 65×512×512 (2.7s)

512×512 Text-to-Image Generation

📰 News

[2024.05.27] 🚀🚀🚀 We are launching Open-Sora Plan v1.1.0, which significantly improves video quality and length, and is fully open source! Please check out our latest report.

[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.

View more

[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.

💪 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
  1. Setup the codebase and train an un-conditional model on a landscape dataset.
  2. Train models that boost resolution and duration.
  • Extensions
  1. Conduct text2video experiments on landscape dataset.
  2. Train the 1080p model on video2text dataset.
  3. Control model with more conditions.
✊ Todo

Setup the codebase and train an unconditional model on landscape dataset

  • Fix typos & Update readme. 🤝 Thanks to @mio2333, @CreamyLong, @chg0901, @Nyx-177, @HowardLi1984, @sennnnn, @Jason-fan20
  • Setup environment. 🤝 Thanks to @nameless1117
  • Add docker file. ⌛ [WIP] 🤝 Thanks to @Mon-ius, @SimonLeeGit
  • Enable type hints for functions. 🤝 Thanks to @RuslanPeresy, 🙏 [Need your contribution]
  • Resume from checkpoint.
  • Add Video-VQVAE model, which is borrowed from VideoGPT.
  • Support variable aspect ratios, resolutions, durations training on DiT.
  • Support Dynamic mask input inspired by FiT.
  • Add class-conditioning on embeddings.
  • Incorporating Latte as main codebase.
  • Add VAE model, which is borrowed from Stable Diffusion.
  • Joint dynamic mask input with VAE.
  • Add VQVAE from VQGAN. 🙏 [Need your contribution]
  • Make the codebase ready for the cluster training. Add SLURM scripts. 🙏 [Need your contribution]
  • Refactor VideoGPT. 🤝 Thanks to @qqingzheng, @luo3300612, @sennnnn
  • Add sampling script.
  • Add DDP sampling script. ⌛ [WIP]
  • Use accelerate on multi-node. 🤝 Thanks to @sysuyy
  • Incorporate SiT. 🤝 Thanks to @khan-yin
  • Add evaluation scripts (FVD, CLIP score). 🤝 Thanks to @rain305f

Train models that boost resolution and duration

  • Add PI to support out-of-domain size. 🤝 Thanks to @jpthu17
  • Add 2D RoPE to improve generalization ability as FiT. 🤝 Thanks to @jpthu17
  • Compress KV according to PixArt-sigma.
  • Support deepspeed for videogpt training. 🤝 Thanks to @sennnnn
  • Train a low dimension Video-AE, whether it is VAE or VQVAE.
  • Extract offline feature.
  • Train with offline feature.
  • Add frame interpolation model. 🤝 Thanks to @yunyangge
  • Add super resolution model. 🤝 Thanks to @Linzy19
  • Add accelerate to automatically manage training.
  • Joint training with images.
  • Implement MaskDiT technique for fast training. 🙏 [Need your contribution]
  • Incorporate NaViT. 🙏 [Need your contribution]
  • Add FreeNoise support for training-free longer video generation. 🙏 [Need your contribution]

Conduct text2video experiments on landscape dataset.

  • Load pretrained weights from Latte.
  • Implement PeRFlow for improving the sampling process. 🙏 [Need your contribution]
  • Finish data loading, pre-processing utils.
  • Add T5 support.
  • Add CLIP support. 🤝 Thanks to @Ytimed2020
  • Add text2image training script.
  • Add prompt captioner.
    • Collect training data.
      • Need video-text pairs with caption. 🙏 [Need your contribution]
      • Extract multi-frame descriptions by large image-language models. 🤝 Thanks to @HowardLi1984
      • Extract video description by large video-language models. 🙏 [Need your contribution]
      • Integrate captions to get a dense caption by using a large language model, such as GPT-4. 🤝 Thanks to @HowardLi1984
    • Train a captioner to refine captions. 🚀 [Require more computation]

Train the 1080p model on video2text dataset

  • Looking for a suitable dataset, welcome to discuss and recommend. 🙏 [Need your contribution]
  • Add synthetic video created by game engines or 3D representations. 🙏 [Need your contribution]
  • Finish data loading, and pre-processing utils.
  • Support memory friendly training.
    • Add flash-attention2 from pytorch.
    • Add xformers. 🤝 Thanks to @jialin-zhao
    • Support mixed precision training.
    • Add gradient checkpoint.
    • Support for ReBased and Ring attention. 🤝 Thanks to @kabachuha
    • Train using the deepspeed engine. 🤝 Thanks to @sennnnn
  • Train with a text condition. Here we could conduct different experiments: 🚀 [Require more computation]
    • Train with T5 conditioning.
    • Train with CLIP conditioning.
    • Train with CLIP + T5 conditioning (probably costly during training and experiments).
  • Support Chinese. ⌛ [WIP]

Control model with more condition

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

📂 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

  1. Clone this repository and navigate to Open-Sora-Plan folder
git clone https://github.com/PKU-YuanGroup/Open-Sora-Plan
cd Open-Sora-Plan
  1. Install required packages
conda create -n opensora python=3.8 -y
conda activate opensora
pip install -e .
  1. Install additional packages for training cases
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
  1. 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 hf_space.

v1.0.0

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

🤝 Enjoying the Replicate demo and cloud API and Open In Colab, 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.

CausalVideoVAE

Reconstructing

Example:

python examples/rec_imvi_vae.py --video_path test_video.mp4 --rec_path output_video.mp4 --fps 24 --resolution 512 --crop_size 512 --num_frames 128 --sample_rate 1 --ae CausalVAEModel_4x8x8 --model_path pretrained_488_release --enable_tiling --enable_time_chunk

Parameter explanation:

  • --enable_tiling: This parameter is a flag to enable a tiling conv.

Training and Eval

Please refer to the document CausalVideoVAE.

VideoGPT VQVAE

Please refer to the document VQVAE.

Video Diffusion Transformer

Training

sh scripts/text_condition/train_videoae_65x512x512.sh
sh scripts/text_condition/train_videoae_221x512x512.sh
sh scripts/text_condition/train_videoae_513x512x512.sh

💡 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.
  • ShareGPT4Video: Improving Video Understanding and Generation with Better Captions.
  • 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

✏️ 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}
}

Latest DOI

DOI

🤝 Community contributors

最近版本更新:(数据更新于 2024-06-02 09:05:28)

2024-05-27 18:02:22 v1.1.0

2024-04-09 14:43:45 v1.0.0

PKU-YuanGroup/Open-Sora-Plan同语言 Python最近更新仓库

2024-06-21 18:02:49 argilla-io/argilla

2024-06-18 16:26:58 hpcaitech/Open-Sora

2024-06-18 10:45:23 vanna-ai/vanna

2024-06-15 05:16:32 NVIDIA/warp

2024-06-14 10:42:40 1Panel-dev/MaxKB

2024-06-13 22:59:46 mikumifa/biliTickerBuy