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about.py
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@@ -30,7 +30,7 @@ SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adver
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# What does your leaderboard evaluate?
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INTRODUCTION_TEXT = """
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UnlearnDiffAtk is an effective and efficient adversarial prompt generation approach for unlearned diffusion models(DMs). For more details,
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please refer to the [benchmark of UnlearnDiffAtk], visit the [project](https://www.optml-group.com/posts/mu_attack),
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check the [code](https://github.com/OPTML-Group/Diffusion-MU-Attack), and read the [paper](https://arxiv.org/abs/2310.11868).\\
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The prompts were validated by us for undesirable concepts: ([church](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/church.csv),
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[Garbage Truch](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/garbage_truck.csv),
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# What does your leaderboard evaluate?
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INTRODUCTION_TEXT = """
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UnlearnDiffAtk is an effective and efficient adversarial prompt generation approach for unlearned diffusion models(DMs). For more details,
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please refer to the [benchmark of UnlearnDiffAtk](https://huggingface.co/spaces/xinchen9/UnlearnDiffAtk-Benchmark), visit the [project](https://www.optml-group.com/posts/mu_attack),
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check the [code](https://github.com/OPTML-Group/Diffusion-MU-Attack), and read the [paper](https://arxiv.org/abs/2310.11868).\\
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The prompts were validated by us for undesirable concepts: ([church](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/church.csv),
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[Garbage Truch](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/garbage_truck.csv),
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