AI RESEARCH
ACE-LoRA: Adaptive Orthogonal Decoupling for Continual Image Editing
arXiv CS.CV
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ArXi:2605.14948v1 Announce Type: new State-of-the-art diffusion models often rely on parameter-efficient fine-tuning to perform specialized image editing tasks. However, real-world applications require continual adaptation to new tasks while preserving previously learned knowledge. Despite the practical necessity, continual learning for image editing remains largely underexplored. We propose ACE-LoRA, a dynamic regularization framework for continual image editing that effectively mitigates catastrophic forgetting.