Editing Images into Reusable Layers via Compositional Edit-Prior Distillation
High-quality image editing requires precise modification of target regions while preserving the content of unrelated regions. Existing editing models produce flattened outputs that limit precise local manipulation and layer reuse, while decomposition methods typically optimize layer recovery separately from instruction-driven editing. To this end, we introduce LayerEditor, a un
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High-quality image editing requires precise modification of target regions while preserving the content of unrelated regions. Existing editing models produce flattened outputs that limit precise local manipulation and layer reuse, while decomposition methods typically optimize layer recovery separately from instruction-driven editing. To this end, we introduce LayerEditor, a un
This work is highly relevant for ML engineers working on creative AI applications, particularly in graphic design and content creation. It addresses the critical need for more controllable and reusable image editing outputs, moving beyond flattened images to layered compositions.