When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on
  its Contour-following Ability
Wenjie Xuan Yufei Xu Shanshan Zhao Chaoyue Wang Juhua Liu Bo Du Dacheng Tao
ControlNet excels at creating content that closely matches precise contours
in user-provided masks. However, when these masks contain noise, as a frequent
occurrence with non-expert users, the output would include unwanted artifacts.
This paper first highlights the crucial role of controlling the impact of these
inexplicit masks with diverse deterioration levels through in-depth analysis.
Subsequently, to enhance controllability with inexplicit masks, an advanced
Shape-aware ControlNet consisting of a deterioration estimator and a
shape-prior modulation block is devised. The deterioration estimator assesses
the deterioration factor of the provided masks. Then this factor is utilized in
the modulation block to adaptively modulate the model's contour-following
ability, which helps it dismiss the noise part in the inexplicit masks.
Extensive experiments prove its effectiveness in encouraging ControlNet to
interpret inaccurate spatial conditions robustly rather than blindly following
the given contours. We showcase application scenarios like modifying shape
priors and composable shape-controllable generation. Codes are soon available.