title: Set You Straight: Auto-Steering Denoising Trajectories to Sidestep
    Unwanted Concepts
summary: Ensuring the ethical deployment of text-to-image models requires effective
  techniques to prevent the generation of harmful or inappropriate content. While
  concept erasure methods offer a promising solution, existing finetuning-based
  approaches suffer from notable limitations. Anchor-free methods risk disrupting
  sampling trajectories, leading to visual artifacts, while anchor-based methods
  rely on the heuristic selection of anchor concepts. To overcome these
  shortcomings, we introduce a finetuning framework, dubbed ANT, which
  Automatically guides deNoising Trajectories to avoid unwanted concepts. ANT is
  built on a key insight: reversing the condition direction of classifier-free
  guidance during mid-to-late denoising stages enables precise content
  modification without sacrificing early-stage structural integrity. This
  inspires a trajectory-aware objective that preserves the integrity of the
  early-stage score function field, which steers samples toward the natural image
  manifold, without relying on heuristic anchor concept selection. For
  single-concept erasure, we propose an augmentation-enhanced weight saliency map
  to precisely identify the critical parameters that most significantly
  contribute to the unwanted concept, enabling more thorough and efficient
  erasure. For multi-concept erasure, our objective function offers a versatile
  plug-and-play solution that significantly boosts performance. Extensive
  experiments demonstrate that ANT achieves state-of-the-art results in both
  single and multi-concept erasure, delivering high-quality, safe outputs without
  compromising the generative fidelity. Code is available at
  https://github.com/lileyang1210/ANT
paper_authors._id: 6801cd2966aeef19a5cec2a4
paper_authors.name: Leyang Li
paper_authors._id: 6801cd2966aeef19a5cec2a5
paper_authors.name: Shilin Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:38:53.383Z
paper_authors._id: 631c4a23aa346997917bcb89
paper_authors.avatarUrl: /avatars/4a562ba78e6be289049027c27798cc6e.svg
paper_authors.fullname: Shilin Lu
paper_authors.type: user
paper_authors.user: Shilin-LU
paper_authors._id: 6801cd2966aeef19a5cec2a6
paper_authors.name: Yan Ren
paper_authors._id: 6801cd2966aeef19a5cec2a7
paper_authors.name: Adams Wai-Kin Kong
paper_title: Set You Straight: Auto-Steering Denoising Trajectories to Sidestep
    Unwanted Concepts
paper_summary: Ensuring the ethical deployment of text-to-image models requires effective
  techniques to prevent the generation of harmful or inappropriate content. While
  concept erasure methods offer a promising solution, existing finetuning-based
  approaches suffer from notable limitations. Anchor-free methods risk disrupting
  sampling trajectories, leading to visual artifacts, while anchor-based methods
  rely on the heuristic selection of anchor concepts. To overcome these
  shortcomings, we introduce a finetuning framework, dubbed ANT, which
  Automatically guides deNoising Trajectories to avoid unwanted concepts. ANT is
  built on a key insight: reversing the condition direction of classifier-free
  guidance during mid-to-late denoising stages enables precise content
  modification without sacrificing early-stage structural integrity. This
  inspires a trajectory-aware objective that preserves the integrity of the
  early-stage score function field, which steers samples toward the natural image
  manifold, without relying on heuristic anchor concept selection. For
  single-concept erasure, we propose an augmentation-enhanced weight saliency map
  to precisely identify the critical parameters that most significantly
  contribute to the unwanted concept, enabling more thorough and efficient
  erasure. For multi-concept erasure, our objective function offers a versatile
  plug-and-play solution that significantly boosts performance. Extensive
  experiments demonstrate that ANT achieves state-of-the-art results in both
  single and multi-concept erasure, delivering high-quality, safe outputs without
  compromising the generative fidelity. Code is available at
  https://github.com/lileyang1210/ANT
paper_ai_summary: ANT framework fine-tunes text-to-image models to prevent unwanted content generation by guiding denoising trajectories and enhancing parameter saliency.
paper_ai_keywords: concept erasure classifier-free guidance denoising trajectories weight saliency map generative fidelity