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Shilin-LU / MACE

[CVPR 2024] "MACE: Mass Concept Erasure in Diffusion Models" (Official Implementation)
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problem of mass erasing celebrities #16

Open 1wttttw1 opened 1 month ago

1wttttw1 commented 1 month ago

I really appreciate your work and have the following questions that I hope you can answer: 1. Is there quantitative tabular data for the concept of erasing celebrities？ 2. Does mass erasure apply equally to the concept of erasing objects, since I only look erase celebrities and styles primarily in paper?Thanks！

Shilin-LU commented 1 month ago

Thank you for your interest. The table data for celebrity concept erasure has been organized below:

Metric 1-celebrity 5-celebrity 10-celebrity 100-celebrity
ACCe_uce 0.0000 0.0000 0.0042 0.0012
ACCe_fmn 0.1134 0.8719 0.9080 0.9223
ACCe_ac 0.9095 0.8058 0.8238 0.8913
ACCe_esdu 0.0000 0.0171 0.0458 0.0406
ACCe_esdx 0.0155 0.0683 0.1649 0.2784
ACCe_sld 0.0082 0.5292 0.6337 0.8706
ACCe_sd14 0.9143 0.9673 0.9798 0.9648
ACCe_sd20 0.9426 0.9677 0.9388 0.9324
ACCe_mace 0.0040 0.0123 0.0285 0.0430
Metric 1-celebrity 5-celebrity 10-celebrity 100-celebrity
ACCs_uce 0.9413 0.9083 0.8351 0.3790
ACCs_fmn 0.7010 0.9099 0.9091 0.9076
ACCs_ac 0.9213 0.8852 0.8692 0.8905
ACCs_esdu 0.4397 0.0970 0.1584 0.0309
ACCs_esdx 0.8947 0.5773 0.5252 0.2793
ACCs_sld 0.7920 0.7749 0.7644 0.7946
ACCs_sd14 0.9388 0.9388 0.9388 0.9388
ACCs_sd20 0.9233 0.9233 0.9233 0.9233
ACCs_mace 0.9169 0.9173 0.9248 0.8456
Metric 1-celebrity 5-celebrity 10-celebrity 100-celebrity
Hc_uce 0.9698 0.9519 0.9084 0.5495
Hc_fmn 0.7830 0.2246 0.1671 0.1431
Hc_ac 0.1648 0.3185 0.2930 0.1937
Hc_esdu 0.6108 0.1766 0.2718 0.0598
Hc_esdx 0.9375 0.7129 0.6449 0.4027
Hc_sld 0.8807 0.5857 0.4953 0.2226
Hc_sd14 0.1571 0.0632 0.0395 0.0679
Hc_sd20 0.1081 0.0624 0.1148 0.1260
Hc_mace 0.9548 0.9512 0.9476 0.8979
Metric 1-celebrity 5-celebrity 10-celebrity 100-celebrity
FID_uce 13.5177 12.7351 24.7264 106.5688
FID_fmn 13.6419 13.9330 13.9340 13.9503
FID_ac 14.1468 14.0898 13.9782 13.9181
FID_esdu 15.8395 15.0515 15.7929 15.1353
FID_esdx 14.4579 14.2121 14.2611 14.6528
FID_sld 16.3881 17.3339 17.3304 17.5449
FID_sd14 14.0432 14.0432 14.0432 14.0432
FID_sd20 14.8702 14.8702 14.8702 14.8702
FID_mace 13.2838 14.1942 14.1222 12.8215
Metric 1-celebrity 5-celebrity 10-celebrity 100-celebrity
CLIP_uce 31.1635 30.7172 29.1864 19.1703
CLIP_fmn 31.1861 31.3118 31.3088 31.3129
CLIP_ac 31.3601 31.2601 31.2610 31.2329
CLIP_esdu 30.0941 29.3750 29.1833 29.0237
CLIP_esdx 30.6987 30.3136 30.2850 29.8995
CLIP_sld 30.8538 30.9933 30.9737 30.9345
CLIP_sd14 31.3443 31.3443 31.3443 31.3443
CLIP_sd20 31.5326 31.5326 31.5326 31.5326
CLIP_mace 31.0761 30.9399 31.0343 30.2126
Metric 1-celebrity 5-celebrity 10-celebrity 100-celebrity
Ratio_uce 0.9760 0.9400 0.9800 0.7192
Ratio_fmn 0.9520 0.9680 1.0000 0.9940
Ratio_ac 0.9720 0.9680 0.9760 0.9940
Ratio_esdu 0.5760 0.4680 0.5240 0.4724
Ratio_esdx 0.7760 0.6440 0.7520 0.8088
Ratio_sld 0.9800 0.9600 0.9720 0.9796
Ratio_sd14 0.9800 0.9800 0.9880 0.9876
Ratio_sd20 0.9760 0.9920 0.9800 0.9884
Ratio_mace 0.9840 0.9720 0.9800 0.8440
Shilin-LU commented 1 month ago

I really appreciate your work and have the following questions that I hope you can answer: 1. Is there quantitative tabular data for the concept of erasing celebrities？ 2. Does mass erasure apply equally to the concept of erasing objects, since I only look erase celebrities and styles primarily in paper?Thanks！

To erase a large number of objects, the approach is similar to erasing other types of concepts. However, erasing a significant number of objects that form part of the prior knowledge in a pretrained diffusion model can severely damage the generative prior. For this reason, we only conducted our multi-concept erasure experiments on celebrity and style-related concepts, as these are less likely to negatively impact the generative prior.