Removing Undesirable Concepts in Text-to-Image Diffusion Models with Learnable Prompts

• URL: http://arxiv.org/abs/2403.12326v2
• Date: Mon, 15 Jul 2024 01:32:38 GMT
• Title: Removing Undesirable Concepts in Text-to-Image Diffusion Models with Learnable Prompts
• Authors: Anh Bui, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Phung,
• Abstract summary: We propose a novel method to remove undesirable concepts from text-to-image diffusion models by incorporating a learnable prompt into the cross-attention module. This learnable prompt acts as additional memory, capturing the knowledge of undesirable concepts. We demonstrate the effectiveness of our method on the Stable Diffusion model, showcasing its superiority over state-of-the-art erasure methods.
• Score: 23.04942433104886
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Diffusion models have shown remarkable capability in generating visually impressive content from textual descriptions. However, these models are trained on vast internet data, much of which contains undesirable elements such as sensitive content, copyrighted material, and unethical or harmful concepts. Therefore, beyond generating high-quality content, it is crucial to ensure these models do not propagate these undesirable elements. To address this issue, we propose a novel method to remove undesirable concepts from text-to-image diffusion models by incorporating a learnable prompt into the cross-attention module. This learnable prompt acts as additional memory, capturing the knowledge of undesirable concepts and reducing their dependency on the model parameters and corresponding textual inputs. By transferring this knowledge to the prompt, erasing undesirable concepts becomes more stable and has minimal negative impact on other concepts. We demonstrate the effectiveness of our method on the Stable Diffusion model, showcasing its superiority over state-of-the-art erasure methods in removing undesirable content while preserving unrelated elements.

Related papers

• SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders [4.013156524547073]
  Diffusion models can inadvertently generate harmful or undesirable content. Recent machine unlearning approaches offer potential solutions but often lack transparency. We introduce SAeUron, a novel method leveraging features learned by sparse autoencoders.
  arXiv  Detail & Related papers  (2025-01-29T23:29:47Z)
• DuMo: Dual Encoder Modulation Network for Precise Concept Erasure [75.05165577219425]
  We propose our Dual encoder Modulation network (DuMo) which achieves precise erasure of inappropriate target concepts with minimum impairment to non-target concepts. Our method achieves state-of-the-art performance on Explicit Content Erasure, Cartoon Concept Removal and Artistic Style Erasure, clearly outperforming alternative methods.
  arXiv  Detail & Related papers  (2025-01-02T07:47:34Z)
• How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? [91.49559116493414]
  We propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM) It can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Experiments validate that our CIDM surpasses existing custom diffusion models.
  arXiv  Detail & Related papers  (2024-10-23T06:47:29Z)
• Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation [22.3077678575067]
  Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. We propose to identify and preserving concepts most affected by parameter changes, termed as textitadversarial concepts. We demonstrate the effectiveness of our method using the Stable Diffusion model, showing that it outperforms state-of-the-art erasure methods in eliminating unwanted content.
  arXiv  Detail & Related papers  (2024-10-21T03:40:29Z)
• Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models [76.39651111467832]
  We introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model in 3 seconds without necessitating additional fine-tuning. To mitigate inappropriate content potentially represented by derived embeddings, RECE aligns them with harmless concepts in cross-attention layers. The derivation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts.
  arXiv  Detail & Related papers  (2024-07-17T08:04:28Z)
• Pruning for Robust Concept Erasing in Diffusion Models [27.67237515704348]
  We introduce a new pruning-based strategy for concept erasing. Our method selectively prunes critical parameters associated with the concepts targeted for removal, thereby reducing the sensitivity of concept-related neurons. Experimental results show a significant enhancement in our model's ability to resist adversarial inputs.
  arXiv  Detail & Related papers  (2024-05-26T11:42:20Z)
• All but One: Surgical Concept Erasing with Model Preservation in Text-to-Image Diffusion Models [22.60023885544265]
  Large-scale datasets may contain sexually explicit, copyrighted, or undesirable content, which allows the model to directly generate them. Fine-tuning algorithms have been developed to tackle concept erasing in diffusion models. We present a new approach that solves all of these challenges.
  arXiv  Detail & Related papers  (2023-12-20T07:04:33Z)
• Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models? [52.238883592674696]
  Ring-A-Bell is a model-agnostic red-teaming tool for T2I diffusion models. It identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content. Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms.
  arXiv  Detail & Related papers  (2023-10-16T02:11:20Z)
• Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models [63.20512617502273]
  We propose a method called SDD to prevent problematic content generation in text-to-image diffusion models. Our method eliminates a much greater proportion of harmful content from the generated images without degrading the overall image quality.
  arXiv  Detail & Related papers  (2023-07-12T07:48:29Z)
• Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models [79.50701155336198]
  textbfForget-Me-Not is designed to safely remove specified IDs, objects, or styles from a well-configured text-to-image model in as little as 30 seconds. We demonstrate that Forget-Me-Not can effectively eliminate targeted concepts while maintaining the model's performance on other concepts. It can also be adapted as a lightweight model patch for Stable Diffusion, allowing for concept manipulation and convenient distribution.
  arXiv  Detail & Related papers  (2023-03-30T17:58:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.