This is a simplified guide to an AI model called Qwen-Image-Layered maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
Qwen-Image-Layered decomposes images into multiple RGBA layers, creating a representation where each semantic or structural component exists in isolation. This approach differs from traditional image editing models by enabling direct manipulation of individual layers without affecting surrounding content. While qwen-image-edit-plus focuses on direct editing within a single image, Qwen-Image-Layered provides a foundational layer structure that makes subsequent edits more consistent and predictable. The model supports variable-layer decomposition, meaning you can request 3 layers, 8 layers, or any number that suits your needs.
Model inputs and outputs
Qwen-Image-Layered accepts an image and configuration parameters, then generates a set of RGBA layers that reconstruct the original image when composited. The layered output enables precise control over individual elements without the consistency issues that plague traditional inpainting approaches.
Inputs
- Image: An RGBA image to be decomposed into semantic layers
- Number of layers: The desired quantity of layers (variable and flexible)
- Resolution: Bucket resolution options (640 or 1024 pixels recommended)
- Inference steps: Number of diffusion steps (default 50)
- Guidance scale: Classifier-free guidance strength for layer quality
- Caption/Prompt: Optional text description or automatic caption generation
Outputs
- Layer images: Multiple RGBA images stacked as separate layers
- Composite result: The combined result showing how layers reconstruct the original
- Individual layer files: PNG files for each layer with transparency information
Capabilities
The model excels at decomposing complex scenes into editable components. You can recolor individual layers while preserving others, replace specific objects (such as changing a person in one layer while keeping the background intact), modify text in isolated layers, or delete unwanted elements cleanly. Elementary operations become high-fidelity: resizing maintains quality without distortion, repositioning moves objects freely within the canvas, and recoloring affects only the target layer. The decomposition process is recursive, meaning any layer can itself be further decomposed for greater detail control.
What can I use it for?
Qwen-Image-Layered suits professional and creative workflows where consistency matters. Graphic designers can edit layered compositions without accidentally affecting background elements. Content creators can swap objects in specific regions with guaranteed isolation from surrounding pixels. E-commerce platforms can modify product images by editing foreground items independently. Marketing teams can recolor branding elements across multiple assets while preserving photographic backgrounds. For monetization, agencies could offer layer-based image editing services with superior consistency guarantees compared to traditional inpainting. Integration with qwen-image-edit-plus allows layer-specific edits after decomposition for a complete editing pipeline.
Things to try
Experiment with variable-layer decomposition on the same image to see how the model interprets semantic boundaries differently. Try recursive decomposition by taking a single layer and decomposing it further—this reveals how much detail the model preserves at each level. Combine layer operations in sequences: decompose, edit multiple layers independently, then recompose. Test the model on images with overlapping objects to understand how it allocates elements across layers. Try adjusting the guidance scale to see how it affects layer separation quality, and compare results across different resolutions to balance detail against processing speed.