Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models

• URL: http://arxiv.org/abs/2602.01089v1
• Date: Sun, 01 Feb 2026 08:05:45 GMT
• Title: Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models
• Authors: Zhiqi Zhang, Xinhao Zhong, Yi Sun, Shuoyang Sun, Bin Chen, Shu-Tao Xia, Xuan Wang,
• Abstract summary: We propose Differential Vector Erasure (DVE), a training-free concept erasure method specifically designed for flow matching models.<n>Our key insight is that semantic concepts are implicitly encoded in the directional structure of the velocity field governing the generative flow.<n>During inference, DVE selectively removes concept-specific components by projecting the velocity field onto the differential direction, enabling precise concept suppression without affecting irrelevant semantics.
• Score: 49.10620605347065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality images, yet their tendency to reproduce undesirable concepts, such as NSFW content, copyrighted styles, or specific objects, poses growing concerns for safe and controllable deployment. While existing concept erasure approaches primarily focus on DDPM-based diffusion models and rely on costly fine-tuning, the recent emergence of flow matching models introduces a fundamentally different generative paradigm for which prior methods are not directly applicable. In this paper, we propose Differential Vector Erasure (DVE), a training-free concept erasure method specifically designed for flow matching models. Our key insight is that semantic concepts are implicitly encoded in the directional structure of the velocity field governing the generative flow. Leveraging this observation, we construct a differential vector field that characterizes the directional discrepancy between a target concept and a carefully chosen anchor concept. During inference, DVE selectively removes concept-specific components by projecting the velocity field onto the differential direction, enabling precise concept suppression without affecting irrelevant semantics. Extensive experiments on FLUX demonstrate that DVE consistently outperforms existing baselines on a wide range of concept erasure tasks, including NSFW suppression, artistic style removal, and object erasure, while preserving image quality and diversity.

Related papers

• EraseAnything++: Enabling Concept Erasure in Rectified Flow Transformers Leveraging Multi-Object Optimization [18.80236205171204]
  EraseAnything++ is a unified framework for concept erasure in both image and video diffusion models.<n>Our method anchors erasure on key visual representations and propagates it consistently across spatial and temporal dimensions.<n>In the video setting, we further enhance consistency through an anchor-and-propagate mechanism that initializes erasure on reference frames and enforces it throughout subsequent transformer layers.
  arXiv  Detail & Related papers  (2026-03-01T08:13:05Z)
• TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models [0.0]
  Concept erasure aims to remove or suppress specific concept information in a generative model.<n>Trajectory-Constrained Attentional Concept Erasure (TRACE) is a novel method to erase targeted concepts from diffusion models.<n>TRACE achieves state-of-the-art performance, outperforming recent methods such as ANT, EraseAnything, and MACE in terms of removal efficacy and output quality.
  arXiv  Detail & Related papers  (2025-05-29T10:15:22Z)
• When Are Concepts Erased From Diffusion Models? [37.59943248660331]
  In concept erasure, a model is modified to selectively prevent it from generating a target concept.<n>We propose two conceptual models for the erasure mechanism in diffusion models.<n>To assess whether a concept has been truly erased from the model, we introduce a comprehensive suite of independent probing techniques.
  arXiv  Detail & Related papers  (2025-05-22T17:59:09Z)
• CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models [7.68494752148263]
  CURE is a training-free concept unlearning framework that operates directly in the weight space of pre-trained diffusion models.<n>The Spectral Eraser identifies and isolates features unique to the undesired concept while preserving safe attributes.<n>CURE achieves a more efficient and thorough removal for targeted artistic styles, objects, identities, or explicit content.
  arXiv  Detail & Related papers  (2025-05-19T03:53:06Z)
• ACE: Attentional Concept Erasure in Diffusion Models [0.0]
  Attentional Concept Erasure integrates a closed-form attention manipulation with lightweight fine-tuning.<n>We show that ACE achieves state-of-the-art concept removal efficacy and robustness.<n>Compared to prior methods, ACE better balances generality (erasing concept and related terms) and specificity (preserving unrelated content)
  arXiv  Detail & Related papers  (2025-04-16T08:16:28Z)
• Modular Customization of Diffusion Models via Blockwise-Parameterized Low-Rank Adaptation [73.16975077770765]
  Modular customization is essential for applications like concept stylization and multi-concept customization.<n>Instant merging methods often cause identity loss and interference of individual merged concepts.<n>We propose BlockLoRA, an instant merging method designed to efficiently combine multiple concepts while accurately preserving individual concepts' identity.
  arXiv  Detail & Related papers  (2025-03-11T16:10:36Z)
• SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models [56.83154571623655]
  We introduce SPEED, an efficient concept erasure approach that directly edits model parameters.<n>Speedy searches for a null space, a model editing space where parameter updates do not affect non-target concepts.<n>We successfully erase 100 concepts within only 5 seconds.
  arXiv  Detail & Related papers  (2025-03-10T14:40:01Z)
• TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models [53.937498564603054]
  Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images.<n>To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts.<n>We propose TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation.
  arXiv  Detail & Related papers  (2025-03-10T14:37:53Z)
• Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization [48.20360860166279]
  Large-scale diffusion models produce high-quality images but often generate unwanted content, such as sexually explicit or violent content.<n>We propose a novel approach for targeted concept replacing in diffusion models, enabling specific concepts to be removed without affecting non-target areas.<n>Our method introduces a dedicated concept localizer for precisely identifying the target concept during the denoising process, trained with few-shot learning to require minimal labeled data.<n>Within the identified region, we introduce a training-free Dual Prompts Cross-Attention (DPCA) module to substitute the target concept, ensuring minimal disruption to surrounding content.
  arXiv  Detail & Related papers  (2024-12-02T08:05:39Z)
• Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient [20.698305103879232]
  We propose a novel concept domain correction framework named textbfDoCo (textbfDomaintextbfCorrection)<n>By aligning the output domains of sensitive and anchor concepts through adversarial training, our approach ensures comprehensive unlearning of target concepts.<n>We also introduce a concept-preserving gradient surgery technique that mitigates conflicting gradient components, thereby preserving the model's utility while unlearning specific concepts.
  arXiv  Detail & Related papers  (2024-05-24T07:47:36Z)
• Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
  We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
  arXiv  Detail & Related papers  (2024-02-03T11:10:57Z)

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.