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This is the classifier model for predicting the anime-style stable-diffusion-generated images are corrupted or not.

Trained on dataset deepghs/ai_image_corrupted.

Models

Name FLOPS Params Accuracy AUC Confusion Labels
caformer_s36_v0_focal 22.10G 37.21M 95.65% 0.9916 confusion corrupted, normal
caformer_s36_v0_sce 22.10G 37.21M 95.77% 0.9894 confusion corrupted, normal
mobilenetv3_v0_focal_dist 0.63G 4.18M 94.02% 0.9842 confusion corrupted, normal

How to Use

You can use this model with dghs-imgutils.

from imgutils.generic import classify_predict_score

classify_predict_score(
    'sample_image.png',
    repo_id='deepghs/ai_image_corrupted',
    model_name='mobilenetv3_v0_focal_dist',
)
# {'corrupted': 0.7807788848876953, 'normal': 0.2192210853099823}

Note

This model is trained on SD1.5 images, generated by 8 different base models.

So the better solution for this problem is to use some metrics like artstyle embeddings, this model will be deprecated as soon as the artstyle embedding model for anime images is completed.

Citation

@misc{Citation,
  title={AI-Corrupt Score for Anime Images},
  author={narugo1992},
  year={2023},
  howpublished={\url{https://huggingface.co/deepghs/ai_image_corrupted}}
}
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Inference Examples
Inference API (serverless) does not yet support dghs-imgutils models for this pipeline type.

Dataset used to train deepghs/ai_image_corrupted

Spaces using deepghs/ai_image_corrupted 2