  Text-to-image (T2I) diffusion models often inadvertently generate unwanted
concepts such as watermarks and unsafe images. These concepts, termed as the
"implicit concepts", could be unintentionally learned during training and then
be generated uncontrollably during inference. Existing removal methods still
struggle to eliminate implicit concepts primarily due to their dependency on
the model's ability to recognize concepts it actually can not discern. To
address this, we utilize the intrinsic geometric characteristics of implicit
concepts and present the Geom-Erasing, a novel concept removal method based on
the geometric-driven control. Specifically, once an unwanted implicit concept
is identified, we integrate the existence and geometric information of the
concept into the text prompts with the help of an accessible classifier or
detector model. Subsequently, the model is optimized to identify and
disentangle this information, which is then adopted as negative prompts during
generation. Moreover, we introduce the Implicit Concept Dataset (ICD), a novel
image-text dataset imbued with three typical implicit concepts (i.e., QR codes,
watermarks, and text), reflecting real-life situations where implicit concepts
are easily injected. Geom-Erasing effectively mitigates the generation of
implicit concepts, achieving the state-of-the-art results on the Inappropriate
Image Prompts (I2P) and our challenging Implicit Concept Dataset (ICD)
benchmarks.
