WAN 2.7 Image Edit
Generated on PixelDojo with WAN 2.7 Image Edit. Produced by PixelDojo's generation pipeline.
WAN 2.7 Image Edit costs 1 credit per image on its default Standard tier and our scene-swap run finished in about 21 seconds on August 26, 2026, returning a 2048 by 2048 file. It takes a reference image and a text instruction and can fuse up to 9 images into a single edit. This page covers the price, the measured speed, what the default run produced, and how it compares with the older WAN 2.6 edit endpoint and the pricier Pro tier on the same model.
The default edit run, one credit
Every example below was produced on PixelDojo. Hover to see the prompt.
Place this exact woman on a bicycle crossing a rainy city crosswalk at dusk, neon shop signs reflecting in puddles, cinematic wide shot, keep her face, glasses, red curly hair, denim jacket and yellow shirt identical
WAN 2.7 Image Edit
What changed since the WAN 2.6 edit endpoint
1 credit by default, 2 on Pro
Leaving model unset resolves to the Standard tier at 1 credit. Setting model to wan-2.7-pro doubles the price to 2 credits for the same request shape.
Up to 9 reference images, not 4
WAN 2.6's edit endpoint caps at 4 input images. This one accepts up to 9, more headroom for multi-subject fusion edits.
Higher default resolution
Our run returned a 2048 by 2048 image against the 1280 by 1280 WAN 2.6 edit produced on the same character-scene prompt. Edit mode on WAN 2.7 caps at 2K on both tiers; 4K is reserved for text-to-image on Pro.
Identity carried through cleanly
The prompt named five things to keep unchanged: face, glasses, red curly hair, denim jacket, yellow shirt. All five showed up correctly in the new rainy street scene, along with a period-styled bicycle the model added on its own.
Interactive bounding-box editing is available
An optional bbox_list parameter can target edits to a specific region of one or more input images instead of rewriting the whole frame. Our run did not use it.
No standalone tool page
Like its WAN 2.6 counterpart, WAN 2.7 Image Edit has no dedicated dashboard page. It is the edit action inside the main WAN 2.7 tool page and Canvas.
Why Choose Pixel Dojo for WAN 2.7 Image Edit
Professional-quality results with cutting-edge AI technology
1 credit by default
The Standard tier price for a single edit or a fusion edit of up to 9 images.
About 21 seconds, measured
Timed on August 26, 2026 from request to finished file, faster than the same edit on the Pro tier in our same-day test.
2048 by 2048 output at the base price
Double the pixel dimensions of WAN 2.6's edit output, at the same 1 credit cost.
How It Works
How we ran it, so you can repeat it:
Send one reference image and leave model unset
Omitting the model field resolves to the 1 credit Standard tier rather than the 2 credit Pro tier.
Name every identity detail to preserve
We listed face, glasses, hair color, jacket, and shirt explicitly, and all five carried through into the new scene.
Run it over the API
POST to /api/v1/models/wan-2.7-image-edit/run with the prompt and image_urls. Ours took about 21 seconds and charged 1 credit.
Loved by creators on PixelDojo
Real feedback from people using PixelDojo, pulled from our in-product surveys.
Great for all your ideas use it encourage more people to use it
Because it is awesome
exceptional quality and great overall design of platform and interface. very intuative. love the creative freedom.
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Common Questions
Everything you need to know about WAN 2.7 Image Edit
What does WAN 2.7 Image Edit cost by default?
1 credit per image on the Standard tier, which is what runs if you do not set the model field. The Pro tier costs 2 credits for the same request shape.
How fast is WAN 2.7 Image Edit?
About 21 seconds in our August 26, 2026 run on the default Standard tier, measured from submission to the finished file. Real turnaround moves with queue load.
How many reference images can it take?
Up to 9 per request, more than double the 4-image cap on WAN 2.6's edit endpoint. That supports larger multi-subject fusion edits.
What resolution does it output?
2048 by 2048 by default in our test, the 2K tier. Edit mode caps at 2K on both Standard and Pro; 4K is reserved for Pro's text-to-image mode.
How does it compare to WAN 2.6 Image Edit?
WAN 2.6's edit endpoint has one flat price and a 4-image cap. This one adds a Pro tier, accepts up to 9 images, and returned a sharper 2048 by 2048 file against WAN 2.6's 1280 by 1280 on the identical prompt and reference image.
How do I call it from the API?
POST to /api/v1/models/wan-2.7-image-edit/run with your API key, a prompt, and an image_urls array. Omit model for the 1 credit Standard tier, or set it to wan-2.7-pro for the 2 credit tier.