Diffusion-based text-to-image (T2I) models generate high-fidelity images for
given textual prompts. They are trained on large datasets scraped from the
Internet, potentially containing unacceptable concepts (e.g., copyright
infringing or unsafe). Retraining T2I models after filtering out unacceptable
concepts in the training data is inefficient and degrades