Papers › Erasing Undesirable Influence in Diffusion Models

Erasing Undesirable Influence in Diffusion Models

11 Jan 2024CVPR 2025 1arXiv:2401.05779archive 2025-07-28

Jing Wu, Trung Le, Munawar Hayat, Mehrtash Harandi

Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. Although various techniques have been proposed to mitigate unwanted influences in diffusion models while preserving overall performance, achieving a balance between these goals remains challenging. In this work, we introduce EraseDiff, an algorithm designed to preserve the utility of the diffusion model on retained data while removing the unwanted information associated with the data to be forgotten. Our approach formulates this task as a constrained optimization problem using the value function, resulting in a natural first-order algorithm for solving the optimization problem. By altering the generative process to deviate away from the ground-truth denoising trajectory, we update parameters for preservation while controlling constraint reduction to ensure effective erasure, striking an optimal trade-off. Extensive experiments and thorough comparisons with state-of-the-art algorithms demonstrate that EraseDiff effectively preserves the model's utility, efficacy, and efficiency.

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Code

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jingwu321/erasediff officialmentioned in papermentioned on GitHubpytorch report

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12 samples harvested; 11 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran · our draft was wrong
3ran · fixture could not drive it
6ran
1unverified

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GetImageFolderLoader jingwu321/erasediff/ddpm/classifier_evaluation.py official repository ran no licence file found · pointer only · 29136f2953d790e8 · report
get_param jingwu321/erasediff/sd/train_scripts/erasediff.py official repository ran no licence file found · pointer only · 2ccf562d07794d65 · report
get_timestep_embedding jingwu321/erasediff/ddpm/models/diffusion.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · cb49209c125de1b4 · report
moving_average jingwu321/erasediff/sd/train_scripts/erasediff.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · f398bc270e4f4ffe · report
noise_estimation_loss jingwu321/erasediff/ddpm/functions/losses.py official repository ran no licence file found · pointer only · 36a45cceb1209d2f · report
noise_estimation_loss_conditional jingwu321/erasediff/ddpm/functions/losses.py official repository ran no licence file found · pointer only · ca8a1bf21579d253 · report
nonlinearity jingwu321/erasediff/ddpm/models/diffusion.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 3137073275f8c21a · report
prob_mask_like jingwu321/erasediff/ddpm/models/diffusion.py official repository ran · fixture could not drive it no licence file found · pointer only · 3c6433dd421724e0 · report
read_images_folder jingwu321/erasediff/ddpm/evaluator.py official repository ran no licence file found · pointer only · 0bae0d076b1ae674 · report
set_param jingwu321/erasediff/sd/train_scripts/erasediff.py official repository ran no licence file found · pointer only · 8168d4d8419b92c3 · report
all_but_one_class_dataset jingwu321/erasediff/ddpm/save_base_dataset.py official repository unverified no licence file found · pointer only · d278a27b738c2136 · report
sample_model identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · 15e005b12a64650d · report

Tasks

DenoisingImage GenerationMemorizationUnconditional Image Generation

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Methods

Diffusion

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