Browse ModelsAlibabaAlibaba Wan 2.7 Image Edit

Alibaba Wan 2.7 Image Edit

Alibaba Wan 2.7 Image Edit

Playground

Try it on WavespeedAI!

Alibaba WAN 2.7 Image Edit performs prompt-driven image editing with multi-image reference support. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

Alibaba Wan 2.7 Image Edit

Alibaba Wan 2.7 Image Edit (alibaba/wan-2.7/image-edit) is a prompt-driven image-to-image editing model for making targeted changes to an existing image. Upload one or more reference images, describe the edit in plain English, and the model returns an updated image while aiming to preserve the original structure, subject identity, and composition.

It’s a strong fit for fast creative iteration: changing clothing, colors, materials, background mood, adding/removing simple objects, and applying style adjustments without rebuilding the entire scene.

Why it stands out

  • Prompt-based edits with strong intent-following for common creative workflows.
  • Designed to preserve composition and key subject features while applying localized changes.
  • Multi-image reference support (1–9 inputs) for style/subject/background guidance and fusion edits.
  • Seed control for repeatable outputs and more consistent iteration.

Capabilities

  • Image-to-image editing from natural-language instructions
  • Multi-image reference editing (1–9 inputs for flexible workflows)
  • Style shifts, background swaps, object addition/removal, and material/color changes
  • More stable iterative refinement when using a fixed seed

Parameters

ParameterDescription
prompt*The edit instruction describing what to change and what to keep (e.g., “change the jacket to leather, keep face and pose unchanged”).
images*One or more input images to edit (1–9 images, uploaded files or public URLs).
seedOptional integer for reproducibility; use a fixed seed to iterate with smaller prompt changes (-1 for random).

How to use

  1. Upload one or more images (the main image to edit; optionally add style/background references).

  2. Write a clear prompt with two parts:

    • What to change (the edit)
    • What must stay the same (constraints) Example: “Replace the background with a rainy Tokyo street at night, keep the person’s face and pose unchanged.”

  3. Optional: set a fixed seed to make iterations more comparable.

  4. Run the model, preview the output, and iterate step-by-step if needed.

Pricing

  • $0.03 per run

Notes

  • If edits spill into areas you want to preserve, strengthen constraints: “keep the face unchanged”, “keep the background intact”, “do not alter the text”.

  • If outputs look inconsistent, try:

    • simplifying the prompt
    • using a fixed seed
    • iterating with smaller changes

Related Models

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/alibaba/wan-2.7/image-edit" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "size": "1024*1024",
    "seed": -1
}'

# 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
imagesarrayYes[]1 ~ 9 itemsList of URLs of input images for editing (1-9 images).
promptstringYes-The positive prompt for the generation.
sizestringNo1024*1024768 ~ 2048 per dimensionThe size of the generated image in pixels (width*height).
seedintegerNo-1-1 ~ 2147483647The random seed to use for the generation. -1 means a random seed will be used.

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.outputsobjectArray of URLs to the generated content.
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
Alibaba Wan 2.6 Video ExtendAlibaba Wan 2.7 Image Edit Pro
© 2025 WaveSpeedAI. All rights reserved.