Model reference · open weights

RepVideo

Video Vchitect Text→video 1 build Open weights 0 dl/mo

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.

  • RepVideo is a text-to-video model developed by Vchitect and published as Vchitect/RepVideo.
  • The model card states that the code is licensed under Apache-2.0, which allows for free commercial usage.
  • The card does not specify the model size, context length, or supported languages.

Summary of the Vchitect/RepVideo model card, 2026-10-01

What it is

Released byVchitect
Released2025-01-14
VRAM23.4 GB for the weights

What it runs on

Memory and cards for RepVideo (BF16)

23.4 GBweights, file size
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GB … RTX 4090 24 GBdoes not fit
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What Vchitect says about RepVideo

Read the model card

:astonished: Gallery

Installation

1. Create a conda environment and download models

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

Inference

cd sat
bash run.sh

BibTeX

@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}
}

🔑 License

This code is licensed under Apache-2.0. The framework is fully open for academic research and also allows free commercial usage.

Disclaimer

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

How video models work

Prompt / imagestart pointTemporal diffusionframes over timeVideoMP4 clipA video model generates a sequence of coherent frames from your prompt or a starting image.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. How to run this model is on its model card.

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