metadata

language: zh
license: creativeml-openrail-m
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - zh
  - Chinese
  - Anime
inference: true
widget:
  - text: 1个女孩,美丽,可爱
    example_title: 1个女孩
  - text: 1个男孩,帅气脸
    example_title: 1个男孩
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  license further specifying rights and usage.

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  harmful outputs or content 

  2. IDEA-CCNL claims no rights on the outputs you generate, you are free to use
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Taiyi-Stable-Diffusion-1B-Chinese-v0.1

简介 Brief Introduction

首个开源的中文Stable Diffusion动漫模型，基于100万筛选过的动漫中文图文对训练。

The first open source Chinese Stable diffusion Anime model, which was trained on 100w filtered Anime Chinese image-text pairs.

模型分类 Model Taxonomy

需求 Demand	任务 Task	系列 Series	模型 Model	参数 Parameter	额外 Extra
特殊 Special	多模态 Multimodal	太乙 Taiyi	Stable Diffusion	1B	Chinese

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

铁马冰河入梦来，3D绘画。	飞流直下三千尺，油画。	女孩背影，日落，唯美插画。

Advanced Prompt

铁马冰河入梦来，概念画，科幻，玄幻，3D	中国海边城市，科幻，未来感，唯美，插画。	那人却在灯火阑珊处，色彩艳丽，古风，资深插画师作品，桌面高清壁纸。

使用 Usage

全精度 Full precision

from diffusers import StableDiffusionPipeline

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

prompt = '1个女孩,美丽,可爱'
image = pipe(prompt, guidance_scale=7.5).images[0]  
image.save("1个女孩.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-Anime-Chinese-v0.1", torch_dtype=torch.float16)
pipe.to('cuda')

prompt = '1个女孩,美丽,可爱'
image = pipe(prompt, guidance_scale=7.5).images[0]  
image.save("1个女孩.png")

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    = {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 and Ruyi Gan and Jiaxing Zhang},
  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}},
}

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

简介 Brief Introduction

模型分类 Model Taxonomy

模型信息 Model Information

Result

使用 Usage

全精度 Full precision

半精度 Half precision FP16 (CUDA)

使用手册 Handbook for Taiyi

怎样微调 How to finetune

webui配置 Configure webui

DreamBooth

引用 Citation