AnyLayer: Order-Agnostic Layer Decomposition with Heterogeneous Queries

Recent approaches to image layer decomposition have made substantial progress. However, existing methods typically support limited query modalities or process targets independently, making it difficult to jointly handle heterogeneous queries while maintaining stable query–layer correspondence. We present AnyLayer, a unified framework that maps heterogeneous text, point, box, and mask queries to their corresponding RGBA foreground layers and a background image within a single diffusion process. First, AnyLayer provides a unified prompting paradigm for jointly processing heterogeneous queries. Second, it introduces an order-agnostic layer representation that removes ordinal foreground identities while preserving explicit query–layer correspondence. Third, Hybrid Layer Attention improves multi-layer generation efficiency by reducing redundant cross-layer computation. Experiments demonstrate effective mixed-modality control, stable performance with increasing layer cardinality, high-quality foreground recovery, occlusion-completion, and background results, and efficient training and inference.