Model reference · open weights
RepVideo is an open-weight video model from Vchitect. RepVideo (BF16) weighs 23.4 GB; the smallest configuration that runs it is RTX 5090 32 GB.
Summary of the Vchitect/RepVideo model card, 2026-10-01
What it is
| Released by | Vchitect |
|---|---|
| Released | 2025-01-14 |
| VRAM | 23.4 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB … RTX 4090 24 GB | does not fit | |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
conda create -n RepVid python==3.10
conda activate RepVid
pip install -r requirements.txt
mkdir ckpt
cd ckpt
mkdir t5-v1_1-xxl
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/text_encoder/config.json
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/text_encoder/model-00001-of-00002.safetensors
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/text_encoder/model-00002-of-00002.safetensors
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/text_encoder/model.safetensors.index.json
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/tokenizer/added_tokens.json
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/tokenizer/special_tokens_map.json
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/tokenizer/spiece.model
wget https://huggingface.co/THUDM/CogVideoX-2b/resolve/main/tokenizer/tokenizer_config.json
cd ../
mkdir vae
wget https://cloud.tsinghua.edu.cn/f/fdba7608a49c463ba754/?dl=1
mv 'index.html?dl=1' vae.zip
unzip vae.zip
cd sat
bash run.sh
@article{si2025RepVideo,
title={RepVideo: Rethinking Cross-Layer Representation for Video Generation},
author={Si, Chenyang and Fan, Weichen and Lv, Zhengyao and Huang, Ziqi and Qiao, Yu and Liu, Ziwei},
journal={arXiv 2501.08994},
year={2025}
}
This code is licensed under Apache-2.0. The framework is fully open for academic research and also allows free commercial usage.
We disclaim responsibility for user-generated content. The model was not trained to realistically represent people or events, so using it to generate such content is beyond the model's capabilities. It is prohibited for pornographic, violent and bloody content generation, and to generate content that is demeaning or harmful to people or their environment, culture, religion, etc. Users are solely liable for their actions. The project contributors are not legally affiliated with, nor accountable for users' behaviors. Use the generative model responsibly, adhering to ethical and legal standards.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works
Running it yourself
Rent a machine by the hour. How to run this model is on its model card.