init commit

README.md

@@ -53,22 +53,15 @@ The first open source Chinese Stable diffusion Anime model, which was trained on
 
 ## 模型信息 Model Information
 
-我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集，先用[IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/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](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)作为初始化的text encoder，冻住[stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([论文](https://arxiv.org/abs/2112.10752))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。
+我们将两份动漫数据集(100万低质量数据和1万高质量数据)，基于[IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1](https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1) 模型进行了两阶段的微调训练，计算开销是4 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](https://wukong-dataset.github.io/wukong-dataset/)(100M) 和 [Zero](https://zero.so.com/)(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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/CompVis/stable-diffusion-v1-4)([paper](https://arxiv.org/abs/2112.10752)) 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！
+We use two anime dataset(1 million low-quality data and 10k high-qualty data) for two-staged training the chinese anime model based our pretrained model[IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1](https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1. It takes 100 hours to train this model based on 4 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。  |
-|  ----  | ----  | ----  |
-| ![](result_examples/tiema.png)  | ![](result_examples/feiliu.png)  | ![](result_examples/nvhai.jpg) |
-
-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。 |
-|  ----  | ----  | ----  |
-| ![](result_examples/tiema2.jpg)  | ![](result_examples/chengshi.jpg) | ![](result_examples/naren.jpg) |
+|  1个女孩,绿色头发,毛衣,看向阅图者,上半身,帽子,户外,下雪,高领毛衣   |  1个男生,帅气,微笑,看着阅图者,简单背景,白皙皮肤,衬衫 |
+|  ----  | ----  |
+| ![](result_examples/girl.png)  | ![](result_examples/boy.png)  |
 
 
 ## 使用 Usage
@@ -102,6 +95,12 @@ 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")
 ```
+### webui配置 Configure webui
+非常推荐使用webui的方式使用本模型，webui提供了可视化的界面加上一些高级修图功能。
+
+It is highly recommended to use this model in a webui way. webui provides a visual interface plus some advanced retouching features.
+
+https://github.com/IDEA-CCNL/stable-diffusion-webui/blob/master/README.md
 
 ### 使用手册 Handbook for Taiyi
 
@@ -111,9 +110,6 @@ https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/stable_
 
 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