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  | ![waitan](examples/waitan.jpeg) | ![gf](examples/gf.jpeg) | ![ssh](examples/ssh.jpeg) |
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  | ![cat](examples/cat.jpeg) | ![robot](examples/robot.jpeg) | ![castle](examples/castle.jpeg) |
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- 大概是Huggingface 🤗社区首个开源的Stable diffusion 2 中文模型。该模型基于stable diffusion V2.1模型,在约500万条的中国风格特挑中文数据上进行微调,数据来源于多个开源数据集如[LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/)和一些网络数据。
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- Probably the first open sourced Chinese Stable Diffusion 2 model in Huggingface🤗 community. This model is finetuned based on stable diffusion V2.1 with 5M chinese style filtered data. Dataset is composed of several different chinese open source dataset such as [LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/) and some web data.
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@@ -34,7 +34,7 @@ Text encoder is frozen [lyua1225/clip-huge-zh-75k-steps-bs4096](https://huggingf
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  #### Unet
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- 在特挑的500万中文数据集上训练了150K steps,使用指数移动平均值(EMA)做原绘画能力保留,使模型能够在中文风格和原绘画能力之间获得权衡。
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  Training on 5M chinese style filtered data for 150k steps. Exponential moving average(EMA) is applied to keep the original Stable Diffusion 2 drawing capability and reach a balance between chinese style and original drawing capability.
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  | ![waitan](examples/waitan.jpeg) | ![gf](examples/gf.jpeg) | ![ssh](examples/ssh.jpeg) |
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  | ![cat](examples/cat.jpeg) | ![robot](examples/robot.jpeg) | ![castle](examples/castle.jpeg) |
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+ 大概是Huggingface 🤗社区首个开源的Stable diffusion 2 中文模型。该模型基于[stable diffusion V2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1)模型,在约500万条的中国风格筛选过的中文数据上进行微调,数据来源于多个开源数据集如[LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/)和一些网络数据。
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+ Probably the first open sourced Chinese Stable Diffusion 2 model in Huggingface🤗 community. This model is finetuned based on [stable diffusion V2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) with 5M chinese style filtered data. Dataset is composed of several different chinese open source dataset such as [LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/) and some web data.
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  #### Unet
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+ 在筛选过的的500万中文数据集上训练了150K steps,使用指数移动平均值(EMA)做原绘画能力保留,使模型能够在中文风格和原绘画能力之间获得权衡。
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  Training on 5M chinese style filtered data for 150k steps. Exponential moving average(EMA) is applied to keep the original Stable Diffusion 2 drawing capability and reach a balance between chinese style and original drawing capability.
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