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--- |
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license: mit |
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tags: |
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- pytorch |
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- diffusers |
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- unconditional-image-generation |
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--- |
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# English Version |
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## Model Card for My First pre-trained model -- test2train_anime_face |
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This model is a diffusion model for unconditional image generation of anime style 64*64 face pic. |
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The training set uses [anime-faces](https://huggingface.co/datasets/huggan/anime-faces). This is a dataset consisting of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm in https://github.com/nagadomi/lbpcascade_animeface. |
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Generating multiple pictures at once is prone to broken face. It has been tested that one picture at a time produces the best results and is not prone to broken faces. |
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### Usage |
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```python |
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from diffusers import DDPMPipeline |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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pipeline = DDPMPipeline.from_pretrained('Chilli-b/test2train_amine_face').to(device) |
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image = pipeline().images[0] |
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image |
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``` |
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--- |
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# 中文版 |
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## 这是我创造的第一个预训练模型—— test2train_anime_face 的模型卡。 |
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该模型是一个无条件扩散模型，用于生成尺寸为 64*64 的动漫风格脸部图片。 |
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训练集使用的是[anime-faces](https://huggingface.co/datasets/huggan/anime-faces),这是一个包含从 www.getchu.com 上爬取的21551个动漫脸，然后使用 https://github.com/nagadomi/lbpcascade_animeface 中的动漫脸检测算法进行裁剪的数据集。 |
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一次生成多张容易出现鬼脸。实测每次出一张图的效果最好，不容易出现鬼脸。 |
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### 模型使用 |
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```python |
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from diffusers import DDPMPipeline |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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pipeline = DDPMPipeline.from_pretrained('Chilli-b/test2train_amine_face').to(device) |
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image = pipeline().images[0] |
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image |
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``` |
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