Qwen-Image-Layered - Alibaba's AI Image Editing Model

What is Qwen-Image-Layered?

Qwen-Image-Layered is an AI image editing model developed by Alibaba. It can automatically split ordinary images into independent RGB transparent layers, achieving layered editing functions similar to Photoshop. Through attention mechanisms and positional encoding, it dynamically splits layers, supporting flexible decomposition of 3-10 layers. This allows for scaling, coloring, and other operations on individual layers without affecting other parts. Compared to the "one-size-fits-all" problem of traditional AI image retouching, the model shows in Hugging Face open-source tests that the color reproduction error is only 0.0033 and the transparency accuracy is as high as 0.916, significantly outperforming existing technologies.

The main functions of Qwen-Image-Layered

• Image layeringThis method decomposes a single RGB image into multiple RGBA layers, each with independent semantics, facilitating subsequent editing.
• Independent EditorEach layer can be operated independently, such as resizing, repositioning, and recoloring, without affecting other layers.
• High-fidelity operationSupports high-fidelity basic operations, such as clear object deletion, lossless resizing, and free object movement.
• Flexible decompositionIt supports a variable number of layer decompositions, and can also recursively decompose to achieve infinite decomposition, meeting different editing needs.
• Data PipelineEstablish a pipeline for extracting multi-layer images from Photoshop documents to address the scarcity of high-quality multi-layer training data.

The technical principles of Qwen-Image-Layered

• RGBA-VAEThis provides a foundational framework for the generation and decomposition of multi-layer images by unifying the latent representations of RGB and RGBA images.
• VLD-MMDiT architectureIt employs the MMDiT architecture with variable layer decomposition, which can flexibly decompose an image into different numbers of layers.
• Multi-stage training strategyBy combining a pre-trained image generation model with multi-stage training, it can be adapted to multi-layer image decomposition tasks, thereby improving model performance.
• Data PipelineExtracting and annotating multi-layered images from Photoshop documents to build a high-quality training dataset and solve the problem of data scarcity.
• diffusion modelBased on the diffusion model generation mechanism, the multi-layer representation of the image is gradually reconstructed from the noise, achieving high-quality layer decomposition.

Qwen-Image-Layered project address

• Github repositoryhttps://github.com/QwenLM/Qwen-Image-Layered
• HuggingFace model libraryhttps://huggingface.co/Qwen/Qwen-Image-Layered
• arXiv technical paperhttps://arxiv.org/pdf/2512.15603
• Experience the demo onlinehttps://huggingface.co/spaces/Qwen/Qwen-Image-Layered

Application scenarios of Qwen-Image-Layered

• Advertising designIt can quickly break down an image into multiple layers, making it easy to edit elements in an advertisement independently, such as changing the background or adjusting the product position.
• Post-production of film and televisionThis feature allows for layering of characters, props, and other elements in film and television footage, facilitating the addition of special effects, color correction, and other operations.
• Creative DesignDesigners can easily break down creative images, modify different elements independently, and inspire more creative ideas.
• Image restorationAfter decomposing the image, damaged layers can be repaired separately without affecting other parts, thus improving repair efficiency.
• Educational demonstrationIn teaching, complex images are broken down into simple layers to help students better understand the principles of image composition and editing.