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--- |
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language: zh |
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license: creativeml-openrail-m |
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tags: |
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- diffusion |
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- zh |
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- Chinese |
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--- |
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# Chinese-Style-Stable-Diffusion-2-v0.1 |
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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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# <u>Model Details</u> |
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#### Text Encoder |
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文本编码器使用冻结参数的[lyua1225/clip-huge-zh-75k-steps-bs4096](https://huggingface.co/lyua1225/clip-huge-zh-75k-steps-bs4096)。 |
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Text encoder is frozen [lyua1225/clip-huge-zh-75k-steps-bs4096](https://huggingface.co/lyua1225/clip-huge-zh-75k-steps-bs4096) . |
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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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## <u>Usage</u> |
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因为使用了customized tokenizer, 所以需要优先加载一下tokenizer, 并传入trust_remote_code=True |
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Customized Tokenizer should be loaded first with 'trust_remote_code=True'. |
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```py |
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import torch |
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from diffusers import StableDiffusionPipeline |
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from transformers import AutoTokenizer |
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tokenizer_id = "lyua1225/clip-huge-zh-75k-steps-bs4096" |
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sd2_id = "Midu/chinese-style-stable-diffusion-2-v0.1" |
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, trust_remote_code=True) |
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pipe = StableDiffusionPipeline.from_pretrained(sd2_id, torch_dtype=torch.float16, tokenizer=tokenizer) |
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pipe.to("cuda") |
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image = pipe("赛博朋克风格的城市街道，8K分辨率，CG渲染", guidance_scale=10, num_inference_steps=20).images[0] |
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image.save("cyberpunk.jpeg") |
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``` |
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