WAN 2.7 Image

WAN 2.7 Image is Alibaba's Wan 2.7 image model for text-to-image, image editing, and multi-reference fusion. It supports resolutions up to about 2K (for example 2048×2048), flexible aspect ratios via preset sizes or custom dimensions, and optional coherent image-set generation for the same character or product across multiple shots. A thinking mode is available on some APIs for stronger reasoning on text-to-image requests.

Alibaba also documents WAN 2.7 Image Pro as a higher-tier variant with emphasis on quality and 4K-class output where the host exposes it—the standard and Pro lines differ in caps and options; check the model card for the route you integrate.

Describe the scene in natural language and get a high-quality image. Prompts can be long (many implementations support extensive character budgets), so you can specify composition, lighting, style, and subject detail in one go. Size can be set with presets such as 1K or 2K, or with explicit width-by-height strings for exact aspect ratios—useful for vertical shorts, landscape thumbnails, or square assets.

You can supply reference images together with a text instruction to edit, restyle, or fuse content. The model accepts multiple inputs for style transfer, element swaps, and blending several references into one output. This fits workflows where you already have a product shot, a character reference, or a mood board and want a new composite or variant without starting from a blank canvas.

Image-set mode is designed for coherent batches from one prompt—for example the same character in different seasons, product angles, or storyboard beats. Providers document generating multiple related images in one request when batch APIs are enabled.

Thinking mode is aimed at improved quality for text-to-image by doing more internal reasoning before rendering. A seed can be used when the API supports it for reproducible runs. Together, these help when you need to iterate on small prompt tweaks or lock down a look for a series of images.