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Wannabeasmartguy/test2train_amine_face
test2train_amine_face is a unconditional image generation model from Wannabeasmartguy. Use it for the unconditional image generation task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as mit.
This model is a diffusion model for unconditional image generation of anime style 6464 face pic. The training set uses anime-faces. This is a dataset consisting of 21551 anime faces scraped from www.getchu.com, which…
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From the Hugging Face model README
This model is a diffusion model for unconditional image generation of anime style 64*64 face pic.
The training set uses 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.
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.


from diffusers import DDPMPipeline
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipeline = DDPMPipeline.from_pretrained('Chilli-b/test2train_amine_face').to(device)
image = pipeline().images[0]
image
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。 训练集使用的是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。
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。


from diffusers import DDPMPipeline
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipeline = DDPMPipeline.from_pretrained('Chilli-b/test2train_amine_face').to(device)
image = pipeline().images[0]
image