---
<<<<<<< HEAD
frameworks:
- Pytorch
license: other
tasks:
- image-to-video

#model-type:
##如 gpt、phi、llama、chatglm、baichuan 等
#- gpt

#domain:
##如 nlp、cv、audio、multi-modal
#- nlp

#language:
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
#- cn 

#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr

#tags:
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained

#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
---
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型

SDK下载
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('ZhipuAI/CogVideoX1.1-5B-SAT')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/ZhipuAI/CogVideoX1.1-5B-SAT.git
```

<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
=======
license: other
language:
- en
base_model:
- THUDM/CogVideoX-5b
- THUDM/CogVideoX-5b-I2V
pipeline_tag: image-to-image
---

# CogVideoX1.1-5B-SAT

<p style="text-align: center;">
  <div align="center">
  <img src=https://modelscope.oss-cn-beijing.aliyuncs.com/resource/cogvideologo.svg width="50%"/>
  </div>
  <p align="center">
  <a href="https://huggingface.co/THUDM/CogVideoX1.1-5B-SAT/blob/main/README_zh.md">📄 中文阅读</a> |
  <a href="https://github.com/THUDM/CogVideo">🌐 Github </a> | 
  <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
</p>
<p align="center">
📍 Visit <a href="https://chatglm.cn/video?lang=en?fr=osm_cogvideo">QingYing</a> and <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9">API Platform</a> to experience commercial video generation models.
</p>

CogVideoX is an open-source video generation model originating from [Qingying](https://chatglm.cn/video?fr=osm_cogvideo). CogVideoX1.1 is the upgraded version of the open-source CogVideoX model.

The CogVideoX1.1-5B series model supports **10-second** videos and higher resolutions. The `CogVideoX1.1-5B-I2V` variant supports **any resolution** for video generation.

This repository contains the SAT-weight version of the CogVideoX1.1-5B model, specifically including the following modules:

## Transformer

Includes weights for both I2V and T2V models. Specifically, it includes the following modules:

```
├── transformer_i2v  
│   ├── 1000  
│   │   └── mp_rank_00_model_states.pt  
│   └── latest  
└── transformer_t2v  
    ├── 1000  
    │   └── mp_rank_00_model_states.pt  
    └── latest  
```

Please select the corresponding weights when performing inference.

## VAE

The VAE part is consistent with the CogVideoX-5B series and does not require updating. You can also download it directly from here. Specifically, it includes the following modules:

```
└── vae  
    └── 3d-vae.pt  
```

## Text Encoder

Consistent with the diffusers version of CogVideoX-5B, no updates are necessary. You can also download it directly from here. Specifically, it includes the following modules:

```
├── t5-v1_1-xxl  
   ├── added_tokens.json  
   ├── config.json  
   ├── model-00001-of-00002.safetensors  
   ├── model-00002-of-00002.safetensors  
   ├── model.safetensors.index.json  
   ├── special_tokens_map.json  
   ├── spiece.model  
   └── tokenizer_config.json  


0 directories, 8 files  
```

## Model License

This model is released under the [CogVideoX LICENSE](LICENSE).

## Citation

```
@article{yang2024cogvideox,
  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
  journal={arXiv preprint arXiv:2408.06072},
  year={2024}
}
```

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