We use
Noah-Wukong
(100M) 和
Zero
(23M) as our dataset, and take the image and text pairs with CLIP Score (based on
IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese
) greater than 0.2 as our Training set. We use
IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese
as our init text encoder. To keep the powerful generative capability of stable diffusion and align Chinese concepts with the images, We only train the text encoder and freeze other part of the
stable-diffusion-v1-4
(
paper
) model. It takes 100 hours to train this model based on 32 x A100. This model is a preliminary version and we will update this model continuously and open sourse. Welcome to exchange!
If you are using the resource for your work, please cite the our
paper
:
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
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