TILDE Paper Proposes TILt-based Distributional Erasure for Concept Unlearning in Diffusion Models
A new arxiv paper (July 7, 2026) introduces TILDE, a method for concept unlearning in text-to-image diffusion models that aims to remove unwanted concepts while preserving generation quality, diversity, and semantic coverage on benign prompts. The work addresses privacy, copyright, and safety concerns, with the gold standard being a retain-only model trained from scratch without the unwanted data.
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