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Home/Explore/Wan 2.7 Models/alibaba/wan-2.7/image-edit-pro
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Alibaba WAN 2.7 Pro

alibaba/wan-2.7/image-edit-pro

Alibaba WAN 2.7 Image Edit Pro performs prompt-driven image editing with multi-image reference support and up to 4K output. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Input

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preview

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preview

Idle

Color-match the skirt in Figure 1 according to the colors of the bird in Figure 2, full of artistic sense, keeping the clothing style unchanged and the model unchanged.Props: Folding fan / Thread-bound book / Round-frame glasses Color Palette: Warm yellow / Dark cyan / Sepia

Your request will cost $0.075 per run.

For $1 you can run this model approximately 13 times.

One more thing:

ExamplesView all

Color-match the skirt in Figure 1 according to the colors of the bird in Figure 2, full of artistic sense, keeping the clothing style unchanged and the model unchanged.Props: Folding fan / Thread-bound book / Round-frame glasses Color Palette: Warm yellow / Dark cyan / Sepia
Print this pattern on a T-shirt and a paper tote bag. A female model is showcasing these items. The girl is also wearing a baseball cap with the words 'Be kind' written on it.
[Basic Facial Description] The character's appearance is based on Reference Figure 1, wearing a dark cyan long gown, round gold-rimmed glasses, and holding a folding fan. The background is an old Shanghai study room with wooden bookshelves, warm yellow tones, vintage film texture, soft side lighting, dust particles dancing in the light beams, cultural atmosphere, quiet and restrained. Shot on Hasselblad medium format, 85mm lens, high resolution, cinematic color grading, Wong Kar-wai style. Props: Folding fan / Thread-bound book / Round-frame glasses Color Palette: Warm yellow / Dark cyan / Sepia

README

Alibaba Wan 2.7 Image Edit Pro

Alibaba Wan 2.7 Image Edit Pro (alibaba/wan-2.7/image-edit-pro) is the professional tier of Alibaba's WanXiang 2.7 image-to-image editing model, supporting output resolutions up to 2048×2048 for higher-fidelity results. 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 ideal for production-grade creative workflows where output quality and resolution matter: retouching product shots, high-res background swaps, detailed style transfers, and any editing task where the standard model's resolution isn't enough.

Why it stands out

  • Higher resolution output (up to 2048×2048) Generate edited images at up to 2048×2048 total pixels (512–4096 per dimension) for print-ready assets, large-format displays, and high-DPI screens. Aspect ratio: 1:8–8:1.

  • Prompt-based edits with strong intent-following Designed to preserve composition and key subject features while applying localized changes.

  • Multi-image reference support (1–9 inputs) Upload multiple images for style/subject/background guidance and fusion edits.

  • Seed control for repeatable outputs Use a fixed seed to refine edits with 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
  • Higher-fidelity output for detail-sensitive workflows
  • 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

  • $0.075 per run

Notes

  • Output size is 512–4096 pixels per dimension. Total pixels must be between 768×768 and 2048×2048, with aspect ratio between 1:8 and 8:1.

  • 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

  • Higher resolutions will take longer to generate than standard sizes.

Related Models

  • Wan 2.7 Image Edit — Standard version at lower cost for everyday editing needs.
  • Alibaba Wan 2.6 Image Edit — Previous generation Wan image-edit model with a similar prompt-driven workflow.
  • Qwen Image Edit — General-purpose AI image editing with strong prompt adherence for everyday creative and product workflows.
  • Google Nano Banana Pro (Edit) — High-fidelity image editing with strong composition preservation and reliable text handling.