Qwen Image Layered

Playground

Qwen-Image Layered is a unified image-layer decomposition model for prompt-guided compositing. Provide points, boxes, or rough masks to isolate subjects and regions, and the model splits a single image into multiple RGBA layers with clean alpha, soft edges, and correct occlusion order. Ready-to-use REST inference API with fast response, no cold starts, and affordable pricing.

Features

qwen-image/layered (Image-to-RGBA Layers)

qwen-image/layered is a layered image decomposition model that splits a single image into multiple clean RGBA layers, enabling flexible compositing, background separation, and layer-based creative editing. It supports an optional prompt for better semantic separation and explicit control over the number of output layers.

🌟 Why it stands out

Controllable layer count: choose exactly how many RGBA layers you want via num_layers.
Clean RGBA outputs: each layer includes transparency for easy compositing and editing.
Prompt-guided separation: optionally describe the scene to improve layer grouping in complex images.
Workflow-friendly: ideal for design iteration, asset cleanup, and creative pipelines.

⚙️ Capabilities

Image-to-multi-layer RGBA decomposition
Transparent background handling per layer (RGBA output)
Optional prompt conditioning for semantic grouping
Works best on images with clear subjects, strong contrast, and limited heavy occlusion

⚙️ Parameters

Parameter	Description
image*	Input image file or public URL.
prompt	Optional caption to guide layer separation (e.g., “a person in front of a building”).
num_layers	Number of RGBA layers to generate (e.g., 4).

💰 Pricing

Reference table:

num_layers	Total Price
1	$0.05
2	$0.10
3	$0.15
4	$0.20
5	$0.25
8	$0.40

How to use

Upload the source image (or provide a public URL).
Set num_layers to the number of layers you want.
Optional: add a prompt to improve semantic separation.
Run the model.
Download the RGBA layers and composite/edit them in your workflow.

💡 Best Use Cases

Layer-based editing and compositing workflows
Background separation and subject isolation
Poster / banner / creative layout design
Rapid asset preparation for marketing and social creatives

📝 Notes

Best results: clear subjects, good lighting, minimal motion blur, and strong foreground/background separation.
Higher num_layers can help break complex scenes into smaller components, but may also create finer splits that require selection/merging in post.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image/layered" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "num_layers": 4,
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

Parameter	Type	Required	Default	Range	Description
image	string	Yes		-	The image to decompose into layers.
prompt	string	No		-	A text description of the image content.
num_layers	integer	No	4	1 ~ 8	The number of layers to generate (1-8).
enable_sync_mode	boolean	No	false	-	If set to true, the function will wait for the result to be generated before returning the response. This property is only available through the API.
enable_base64_output	boolean	No	false	-	If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Response Parameters

Parameter	Type	Description
code	integer	HTTP status code (e.g., 200 for success)
message	string	Status message (e.g., “success”)
data.id	string	Unique identifier for the prediction, Task Id
data.model	string	Model ID used for the prediction
data.outputs	array	Array of URLs to the generated content (empty when status is not `completed`)
data.urls	object	Object containing related API endpoints
data.urls.get	string	URL to retrieve the prediction result
data.has_nsfw_contents	array	Array of boolean values indicating NSFW detection for each output
data.status	string	Status of the task: `created`, `processing`, `completed`, or `failed`
data.created_at	string	ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.error	string	Error message (empty if no error occurred)
data.timings	object	Object containing timing details
data.timings.inference	integer	Inference time in milliseconds

Result Request Parameters

Parameter	Type	Required	Default	Description
id	string	Yes	-	Task ID

Result Response Parameters

Parameter	Type	Description
code	integer	HTTP status code (e.g., 200 for success)
message	string	Status message (e.g., “success”)
data	object	The prediction data object containing all details
data.id	string	Unique identifier for the prediction, the ID of the prediction to get
data.model	string	Model ID used for the prediction
data.outputs	string	Array of URLs to the generated layer images (empty when status is not completed).
data.urls	object	Object containing related API endpoints
data.urls.get	string	URL to retrieve the prediction result
data.status	string	Status of the task: `created`, `processing`, `completed`, or `failed`
data.created_at	string	ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.error	string	Error message (empty if no error occurred)
data.timings	object	Object containing timing details
data.timings.inference	integer	Inference time in milliseconds

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