WaveSpeedAI APIWavespeed AIQwen Image Edit 2511

Qwen Image Edit 2511

Qwen Image Edit 2511

Playground

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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

  • Stronger multi-person consistency Handles group photos and multi-subject scenes with better stability and fewer identity swaps.

  • Integrated popular community LoRA styles Built-in style options for common community aesthetics without extra setup (availability depends on the endpoint).

  • Better industrial & product editing Cleaner structure, surfaces, and product geometry for design mockups and marketing visuals.

  • Reduced drift across edits Improved identity and subject consistency when making iterative or larger edits.

  • Improved geometric reasoning More reliable structural transformations and shape-aware editing.


Core capabilities

  • 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.
  • Precise text editing (when applicable) Add, delete, or replace on-image text while keeping natural typography behavior (spacing, alignment, style).

  • 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

ParameterDescription
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

  1. Add your base image as the first item in images (you should see a preview in the UI).
  2. Optionally add 1–2 more reference images (maximum 3 total) to guide style, subject details, or composition.
  3. Write a clear prompt describing the edit and constraints (examples: “keep face unchanged”, “keep pose”, “keep background”).
  4. Run the model and review the result.
  5. 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.


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

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
imagesarrayYes[]1 ~ 3 itemsThe images to edit. A maximum of 3 reference images can be uploaded.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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