Taiyi-Stable-Diffusion-1B-Chinese-v0.1

Taiyi-Stable-Diffusion-1B-Chinese-v0.1

• Main Page:Fengshenbang
• Github: Fengshenbang-LM

简介 Brief Introduction

首个开源的中文Stable Diffusion模型，基于0.2亿筛选过的中文图文对训练。

The first open source Chinese Stable diffusion, which was trained on 20M filtered Chinese image-text pairs.

在线体验 Gradio Web UI

可以在Taiyi-Stable-Diffusion-Chinese体验我们的模型。

We support a Gradio Web UI to run Taiyi-Stable-Diffusion-1B-Chinese-v0.1: Taiyi-Stable-Diffusion-Chinese

简介 Brief Introduction

首个开源的中英双语Stable Diffusion模型，基于0.2亿筛选过的中文图文对训练。

模型分类 Model Taxonomy

模型信息 Model Information

我们将Noah-Wukong数据集(100M)和Zero数据集(23M)用作预训练的数据集，先用IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese对这两个数据集的图文对相似性进行打分，取CLIP Score大于0.2的图文对作为我们的训练集。 我们使用IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese作为初始化的text encoder，冻住stable-diffusion-v1-4(论文)模型的其他部分，只训练text encoder，以便保留原始模型的生成能力且实现中文概念的对齐。该模型目前在0.2亿图文对上训练了一个epoch。 我们在 32 x A100 训练了大约100小时。该版本只是一个初步的版本，我们将持续优化并开源后续模型，欢迎交流。

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！

Result

Basic Prompt

Advanced Prompt

使用 Usage

全精度 Full precision

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1").to("cuda")

prompt = '飞流直下三千尺，油画'
image = pipe(prompt, guidance_scale=7.5).images[0]  
image.save("飞流.png")

半精度 Half precision FP16 (CUDA)

添加 torch_dtype=torch.float16 和 device_map="auto" 可以快速加载 FP16 的权重，以加快推理速度。 更多信息见 the optimization docs。

# !pip install git+https://github.com/huggingface/accelerate
import torch
from diffusers import StableDiffusionPipeline
torch.backends.cudnn.benchmark = True
pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1", torch_dtype=torch.float16)
pipe.to('cuda')

prompt = '飞流直下三千尺，油画'
image = pipe(prompt, guidance_scale=7.5).images[0]  
image.save("飞流.png")

使用手册 Handbook for Taiyi

https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/stable_diffusion_chinese/taiyi_handbook.md

怎样微调 How to finetune

https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/finetune_taiyi_stable_diffusion

webui配置 Configure webui

https://github.com/IDEA-CCNL/stable-diffusion-webui/blob/master/README.md

DreamBooth

https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/stable_diffusion_dreambooth

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的总论文：

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}
}

也可以引用我们的网站:

You can also cite our website:

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}