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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+## Open-Sora: Democratizing Efficient Video Production for All
+
+We design and implement **Open-Sora**, an initiative dedicated to **efficiently** producing high-quality video. We hope to make the model,
+tools and all details accessible to all. By embracing **open-source** principles,
+Open-Sora not only democratizes access to advanced video generation techniques, but also offers a
+streamlined and user-friendly platform that simplifies the complexities of video generation.
+With Open-Sora, our goal is to foster innovation, creativity, and inclusivity within the field of content creation.
+
+Github: https://github.com/hpcaitech/Open-Sora
+
+## 🔆 Reports
+
+- **[Tech Report of Open-Sora 2.0](https://github.com/hpcaitech/Open-Sora-Demo/blob/main/paper/Open_Sora_2_tech_report.pdf)**
+- **[Step by step to train or finetune your own model](docs/train.md)**
+- **[Step by step to train and evaluate an video autoencoder](docs/ae.md)**
+- **[Visit the high compression video autoencoder](docs/hcae.md)**
+- Reports of previous version (better see in according branch):
+  - [Open-Sora 1.3](docs/report_04.md): shift-window attention, unified spatial-temporal VAE, etc.
+  - [Open-Sora 1.2](docs/report_03.md), [Tech Report](https://arxiv.org/abs/2412.20404): rectified flow, 3d-VAE, score condition, evaluation, etc.
+  - [Open-Sora 1.1](docs/report_02.md): multi-resolution/length/aspect-ratio, image/video conditioning/editing, data preprocessing, etc.
+  - [Open-Sora 1.0](docs/report_01.md): architecture, captioning, etc.
+
+📍 Since Open-Sora is under active development, we remain different branchs for different versions. The latest version is [main](https://github.com/hpcaitech/Open-Sora). Old versions include: [v1.0](https://github.com/hpcaitech/Open-Sora/tree/opensora/v1.0), [v1.1](https://github.com/hpcaitech/Open-Sora/tree/opensora/v1.1), [v1.2](https://github.com/hpcaitech/Open-Sora/tree/opensora/v1.2), [v1.3](https://github.com/hpcaitech/Open-Sora/tree/opensora/v1.3).
+
+## Quickstart
+
+### Installation
+
+```bash
+# create a virtual env and activate (conda as an example)
+conda create -n opensora python=3.10
+conda activate opensora
+
+# download the repo
+git clone https://github.com/hpcaitech/Open-Sora
+cd Open-Sora
+
+# Ensure torch >= 2.4.0
+pip install -v . # for development mode, `pip install -v -e .`
+pip install xformers==0.0.27.post2 --index-url https://download.pytorch.org/whl/cu121 # install xformers according to your cuda version
+pip install flash-attn --no-build-isolation
+```
+
+Optionally, you can install flash attention 3 for faster speed.
+
+```bash
+git clone https://github.com/Dao-AILab/flash-attention # 4f0640d5
+cd flash-attention/hopper
+python setup.py install
+```
+
+### Model Download
+
+Our 11B model supports 256px and 768px resolution. Both T2V and I2V are supported by one model. 🤗 [Huggingface](https://huggingface.co/hpcai-tech/Open-Sora-v2) 🤖 [ModelScope](https://modelscope.cn/models/luchentech/Open-Sora-v2).
+
+Download from huggingface:
+
+```bash
+pip install "huggingface_hub[cli]"
+huggingface-cli download hpcai-tech/Open-Sora-v2 --local-dir ./ckpts
+```
+
+Download from ModelScope:
+
+```bash
+pip install modelscope
+modelscope download hpcai-tech/Open-Sora-v2 --local_dir ./ckpts
+```
+
+### Text-to-Video Generation
+
+Our model is optimized for image-to-video generation, but it can also be used for text-to-video generation. To generate high quality videos, with the help of flux text-to-image model, we build a text-to-image-to-video pipeline. For 256x256 resolution:
+
+```bash
+# Generate one given prompt
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_256px.py --save-dir samples --prompt "raining, sea"
+
+# Generation with csv
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_256px.py --save-dir samples --dataset.data-path assets/texts/example.csv
+```
+
+For 768x768 resolution:
+
+```bash
+# One GPU
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_768px.py --save-dir samples --prompt "raining, sea"
+
+# Multi-GPU with colossalai sp
+torchrun --nproc_per_node 8 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_768px.py --save-dir samples --prompt "raining, sea"
+```
+
+You can adjust the generation aspect ratio by `--aspect_ratio` and the generation length by `--num_frames`. Candidate values for aspect_ratio includes `16:9`, `9:16`, `1:1`, `2.39:1`. Candidate values for num_frames should be `4k+1` and less than 129.
+
+You can also run direct text-to-video by:
+
+```bash
+# One GPU for 256px
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/256px.py --prompt "raining, sea"
+# Multi-GPU for 768px
+torchrun --nproc_per_node 8 --standalone scripts/diffusion/inference.py configs/diffusion/inference/768px.py --prompt "raining, sea"
+```
+
+### Image-to-Video Generation
+
+Given a prompt and a reference image, you can generate a video with the following command:
+
+```bash
+# 256px
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/256px.py --cond_type i2v_head --prompt "A plump pig wallows in a muddy pond on a rustic farm, its pink snout poking out as it snorts contentedly. The camera captures the pig's playful splashes, sending ripples through the water under the midday sun. Wooden fences and a red barn stand in the background, framed by rolling green hills. The pig's muddy coat glistens in the sunlight, showcasing the simple pleasures of its carefree life." --ref assets/texts/i2v.png
+
+# 256px with csv
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/256px.py --cond_type i2v_head --dataset.data-path assets/texts/i2v.csv
+
+# Multi-GPU 768px
+torchrun --nproc_per_node 8 --standalone scripts/diffusion/inference.py configs/diffusion/inference/768px.py --cond_type i2v_head --dataset.data-path assets/texts/i2v.csv
+```
+
+## Advanced Usage
+
+### Motion Score
+
+During training, we provide motion score into the text prompt. During inference, you can use the following command to generate videos with motion score (the default score is 4):
+
+```bash
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_256px.py --save-dir samples --prompt "raining, sea" --motion-score 4
+```
+
+We also provide a dynamic motion score evaluator. After setting your OpenAI API key, you can use the following command to evaluate the motion score of a video:
+
+```bash
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_256px.py --save-dir samples --prompt "raining, sea" --motion-score dynamic
+```
+
+| Score | 1                                                                                                       | 4                                                                                                       | 7                                                                                                       |
+| ----- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- |
+|       | <img src="https://github.com/hpcaitech/Open-Sora-Demo/blob/main/demo/v2.0/motion_score_1.gif" width=""> | <img src="https://github.com/hpcaitech/Open-Sora-Demo/blob/main/demo/v2.0/motion_score_4.gif" width=""> | <img src="https://github.com/hpcaitech/Open-Sora-Demo/blob/main/demo/v2.0/motion_score_7.gif" width=""> |
+
+### Prompt Refine
+
+We take advantage of ChatGPT to refine the prompt. You can use the following command to refine the prompt. The function is available for both text-to-video and image-to-video generation.
+
+```bash
+export OPENAI_API_KEY=sk-xxxx
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_256px.py --save-dir samples --prompt "raining, sea" --refine-prompt True
+```
+
+### Reproductivity
+
+To make the results reproducible, you can set the random seed by:
+
+```bash
+torchrun --nproc_per_node 1 --standalone scripts/diffusion/inference.py configs/diffusion/inference/t2i2v_256px.py --save-dir samples --prompt "raining, sea" --sampling_option.seed 42 --seed 42
+```
+
+Use `--num-sample k` to generate `k` samples for each prompt.
+
+## Computational Efficiency
+
+We test the computational efficiency of text-to-video on H100/H800 GPU. For 256x256, we use colossalai's tensor parallelism. For 768x768, we use colossalai's sequence parallelism. All use number of steps 50. The results are presented in the format: $\color{blue}{\text{Total time (s)}}/\color{red}{\text{peak GPU memory (GB)}}$
+
+| Resolution | 1x GPU                                 | 2x GPUs                               | 4x GPUs                               | 8x GPUs                               |
+| ---------- | -------------------------------------- | ------------------------------------- | ------------------------------------- | ------------------------------------- |
+| 256x256    | $\color{blue}{60}/\color{red}{52.5}$   | $\color{blue}{40}/\color{red}{44.3}$  | $\color{blue}{34}/\color{red}{44.3}$  |                                       |
+| 768x768    | $\color{blue}{1656}/\color{red}{60.3}$ | $\color{blue}{863}/\color{red}{48.3}$ | $\color{blue}{466}/\color{red}{44.3}$ | $\color{blue}{276}/\color{red}{44.3}$ |
+
+## Evaluation
+
+On [VBench](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard), Open-Sora 2.0 significantly narrows the gap with OpenAI’s Sora, reducing it from 4.52% → 0.69% compared to Open-Sora 1.2.
+
+![VBench](https://github.com/hpcaitech/Open-Sora-Demo/blob/main/readme/v2_vbench.png)
+
+Human preference results show our model is on par with HunyuanVideo 14B and Step-Video 30B.
+
+![Win Rate](https://github.com/hpcaitech/Open-Sora-Demo/blob/main/readme/v2_winrate.png)
+
+## Contribution
+
+Thanks goes to these wonderful contributors:
+
+<a href="https://github.com/hpcaitech/Open-Sora/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=hpcaitech/Open-Sora" />
+</a>
+
+If you wish to contribute to this project, please refer to the [Contribution Guideline](./CONTRIBUTING.md).
+
+## Acknowledgement
+
+Here we only list a few of the projects. For other works and datasets, please refer to our report.
+
+- [ColossalAI](https://github.com/hpcaitech/ColossalAI): A powerful large model parallel acceleration and optimization
+  system.
+- [DiT](https://github.com/facebookresearch/DiT): Scalable Diffusion Models with Transformers.
+- [OpenDiT](https://github.com/NUS-HPC-AI-Lab/OpenDiT): An acceleration for DiT training. We adopt valuable acceleration
+  strategies for training progress from OpenDiT.
+- [PixArt](https://github.com/PixArt-alpha/PixArt-alpha): An open-source DiT-based text-to-image model.
+- [Flux](https://github.com/black-forest-labs/flux): A powerful text-to-image generation model.
+- [Latte](https://github.com/Vchitect/Latte): An attempt to efficiently train DiT for video.
+- [HunyuanVideo](https://github.com/Tencent/HunyuanVideo/tree/main?tab=readme-ov-file): Open-Source text-to-video model.
+- [StabilityAI VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse-original): A powerful image VAE model.
+- [DC-AE](https://github.com/mit-han-lab/efficientvit): Deep Compression AutoEncoder for image compression.
+- [CLIP](https://github.com/openai/CLIP): A powerful text-image embedding model.
+- [T5](https://github.com/google-research/text-to-text-transfer-transformer): A powerful text encoder.
+- [LLaVA](https://github.com/haotian-liu/LLaVA): A powerful image captioning model based on [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) and [Yi-34B](https://huggingface.co/01-ai/Yi-34B).
+- [PLLaVA](https://github.com/magic-research/PLLaVA): A powerful video captioning model.
+- [MiraData](https://github.com/mira-space/MiraData): A large-scale video dataset with long durations and structured caption.
+
+## Citation
+
+```bibtex
+@software{opensora,
+  author = {Zangwei Zheng and Xiangyu Peng and Tianji Yang and Chenhui Shen and Shenggui Li and Hongxin Liu and Yukun Zhou and Tianyi Li and Yang You},
+  title = {Open-Sora: Democratizing Efficient Video Production for All},
+  month = {March},
+  year = {2024},
+  url = {https://github.com/hpcaitech/Open-Sora}
+}
+```
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=hpcaitech/Open-Sora&type=Date)](https://star-history.com/#hpcaitech/Open-Sora&Date)