Qwen Image Layered | Fast Image Editing API

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
2	$0.050
3	$0.075
4	$0.100
5	$0.125
8	$0.200

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.

Qwen Image Layered API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image/layered with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Qwen Image Layered below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image/layered" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "image": "https://example.com/your-input.jpg",
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "num_layers": 4,
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/qwen-image/layered", {
        "image": "https://example.com/your-input.jpg",
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "num_layers": 4,
        "enable_sync_mode": false,
        "enable_base64_output": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/qwen-image/layered",
    {
    "image": "https://example.com/your-input.jpg",
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "num_layers": 4,
    "enable_sync_mode": false,
    "enable_base64_output": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Qwen Image Layered API — Frequently asked questions

What is the Qwen Image Layered API?

Qwen Image Layered is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. 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. You can call it programmatically or try it from the playground above.

How do I call the Qwen Image Layered API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/qwen-image-layered.

How much does Qwen Image Layered cost per run?

Qwen Image Layered starts at $0.025 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Qwen Image Layered accept?

Key inputs: `prompt`, `image`, `enable_base64_output`, `enable_sync_mode`, `num_layers`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/qwen-image-layered.

How long does Qwen Image Layered take to generate?

Average end-to-end generation time on WaveSpeedAI is around 13 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Qwen Image Layered outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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