Qwen Image Edit 2511
Playground
Try it on WavespeedAI!Qwen Image Edit 2511 is a major upgrade over 2509 for real-world image editing and design. It delivers stronger edit consistency, robust multi-person identity/pose consistency, built-in LoRA styles, enhanced industrial/product design, and improved geometric reasoning for structure-preserving edits. Built for stable production use with a ready-to-use REST API, no cold starts, and predictable pricing.
Features
Qwen-Image-Edit-2511 (20B, MMDiT)
Qwen-Image-Edit-2511 is a high-consistency, production-grade image editing model built on the Qwen-Image 20B (MMDiT) architecture, delivering stronger real-world edits, better identity preservation, and more reliable multi-subject control than earlier releases. It’s designed for fast, prompt-driven edits with stable composition, clean details, and commercial-ready output quality.
What’s new in 2511
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Stronger multi-person consistency Handles group photos and multi-subject scenes with better stability and fewer identity swaps.
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Integrated popular community LoRA styles Built-in style options for common community aesthetics without extra setup (availability depends on the endpoint).
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Better industrial & product editing Cleaner structure, surfaces, and product geometry for design mockups and marketing visuals.
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Reduced drift across edits Improved identity and subject consistency when making iterative or larger edits.
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Improved geometric reasoning More reliable structural transformations and shape-aware editing.
Core capabilities
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Dual-mode editing
- Appearance editing: add/remove/modify elements while keeping other regions visually consistent.
- Semantic editing: global style/pose/scene transformations that preserve intent while allowing broader pixel changes.
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Precise text editing (when applicable) Add, delete, or replace on-image text while keeping natural typography behavior (spacing, alignment, style).
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Style preservation Maintains lighting, palette, and overall look while applying targeted changes.
Best for
- Multi-person projects — group photos, team portraits, event shots
- Industrial & product design — product mockups, packaging tweaks, commercial comps
- Identity-preserving edits — portraits, characters, avatar refinement
- Design & marketing teams — fast iterations, brand-safe edits, localization visuals
- E-commerce & social — product cleanup, background updates, quick visual variations
Example prompts
- Multi-person: Add a third person matching the existing lighting and camera angle.
- Industrial: Convert this product into a clean technical blueprint view with construction lines.
- Identity: Keep the person’s facial features unchanged and replace the background with a modern office.
- Appearance: Add a latte cup in the top-right corner without changing anything else.
- Semantic: Restyle the scene as cyberpunk while keeping the brand logo and layout consistent.
Parameters
| Parameter | Description |
|---|---|
| prompt* | The edit instruction describing what to change and what to keep. |
| images* | Input images to edit or reference. Up to 3 images maximum (the first image is typically treated as the main base image). |
How to use
- Add your base image as the first item in images (you should see a preview in the UI).
- Optionally add 1–2 more reference images (maximum 3 total) to guide style, subject details, or composition.
- Write a clear prompt describing the edit and constraints (examples: “keep face unchanged”, “keep pose”, “keep background”).
- Run the model and review the result.
- Iterate by tightening constraints and making one major edit per run for best consistency.
Supported output formats typically include JPG / PNG / WEBP (as exposed by the endpoint).
Pricing
- $0.03 per edited image
Note
If you’re using image URLs (instead of uploading locally), make sure they’re publicly accessible. If the URL is valid, the interface will display a preview before you run the job.
Related Models
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Qwen Image Edit — AI Image Editing & Inpainting — Prompt-driven image editing for object removal, background replacement, and inpainting with fast iterations and strong instruction following.
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Qwen Image Edit Plus — High-Fidelity Image Editing — Higher-quality image edits with cleaner edges, improved detail retention, and more stable results on complex scenes and textures.
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Google Nano Banana Pro (Edit) — Photoreal Image Editor — High-fidelity image editing optimized for photoreal results, accurate text rendering, and composition-preserving transformations for professional creatives.
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/edit-2511" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | The positive prompt for the generation. | |
| images | array | Yes | [] | 1 ~ 3 items | The images to edit. A maximum of 3 reference images can be uploaded. |
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 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.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 |