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
