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zai-org/CogVideoX1.5-5B-SAT
CogVideoX1.5-5B-SAT is a image-to-video model from zai-org. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. The card lists the license as other.
<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/CogVideoX…
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Updated Nov 8, 2024
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.pt31.4 GB · 77%
From the Hugging Face model README
CogVideoX is an open-source video generation model originating from Qingying. CogVideoX1.5 is the upgraded version of the open-source CogVideoX model.
The CogVideoX1.5-5B series model supports 10-second videos and higher resolutions. The CogVideoX1.5-5B-I2V variant supports any resolution for video generation.
This repository contains the SAT-weight version of the CogVideoX1.5-5B model, specifically including the following modules:
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.
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
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
This model is released under the CogVideoX LICENSE.
@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}
}