  Deep generative models are known to produce undesirable samples such as
harmful content. Traditional mitigation methods include re-training from
scratch, filtering, or editing; however, these are either computationally
expensive or can be circumvented by third parties. In this paper, we take a
different approach and study how to post-edit an already-trained conditional
generative model so that it redacts certain conditionals that will, with high
probability, lead to undesirable content. This is done by distilling the
conditioning network in the models, giving a solution that is effective,
efficient, controllable, and universal for a class of deep generative models.
We conduct experiments on redacting prompts in text-to-image models and
redacting voices in text-to-speech models. Our method is computationally light,
leads to better redaction quality and robustness than baseline methods while
still retaining high generation quality.
