ACE-LoRA: Adaptive Orthogonal Decoupling for Continual Image Editing
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. ACE-LoRA leverages Adaptive Orthogonal Decoupling to identify and orthogonalize task interference, and introduces a Rank-Invariant Historical Information Compression strategy to address scalability issues in contin...
| Authors | Yuehao Liu, Weijia Zhang, Xuanming Shang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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