Qwen-Image-Layered is a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks inherent editability where each layer can be independently manipulated without affecting other content. The model physically isolates semantic or structural components into distinct layers, enabling high-fidelity and consistent editing.

Key features include the ability to decompose images into variable numbers of layers, support for recursive decomposition where any layer can be further decomposed, and independent manipulation of individual layers. The model enables elementary operations such as resizing, repositioning, recoloring, deleting objects cleanly, and moving objects freely within the canvas after decomposition.

The model works by physically isolating semantic or structural components into distinct RGBA layers, which fundamentally ensures consistency across edits. This approach bridges the gap between raster imagery and structured, editable representations by reimagining images as composable layers.

Benefits include enabling intuitive, precise, and robust editing capabilities. The layered structure naturally supports high-fidelity elementary operations and allows for flexible editing workflows where edits are applied exclusively to target layers.

The model is available through multiple platforms including GitHub, Hugging Face, and ModelScope, with a demo available for testing. It's designed for research and practical applications in image editing and manipulation.