  The prevalent use of commercial and open-source diffusion models (DMs) for
text-to-image generation prompts risk mitigation to prevent undesired
behaviors. Existing concept erasing methods in academia are all based on full
parameter or specification-based fine-tuning, from which we observe the
following issues: 1) Generation alternation towards erosion: Parameter drift
during target elimination causes alternations and potential deformations across
all generations, even eroding other concepts at varying degrees, which is more
evident with multi-concept erased; 2) Transfer inability & deployment
inefficiency: Previous model-specific erasure impedes the flexible combination
of concepts and the training-free transfer towards other models, resulting in
linear cost growth as the deployment scenarios increase. To achieve
non-invasive, precise, customizable, and transferable elimination, we ground
our erasing framework on one-dimensional adapters to erase multiple concepts
from most DMs at once across versatile erasing applications. The
concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to
learn targeted erasing, and meantime the alteration and erosion phenomenon is
effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once
obtained, SPMs can be flexibly combined and plug-and-play for other DMs without
specific re-tuning, enabling timely and efficient adaptation to diverse
scenarios. During generation, our Facilitated Transport mechanism dynamically
regulates the permeability of each SPM to respond to different input prompts,
further minimizing the impact on other concepts. Quantitative and qualitative
results across ~40 concepts, 7 DMs and 4 erasing applications have demonstrated
the superior erasing of SPM. Our code and pre-tuned SPMs are available on the
project page https://lyumengyao.github.io/projects/spm.
