Qwen Image 3.0

Positioning Overview

The image editing pipeline of Qwen Image 3.0 Pro makes controllable modifications to an image based on 1–3 reference images, with the focus on preserving the key details of the original image while replacing the scene, style, clothing, or on-image text. It suits design, e-commerce, and content teams that need to iterate repeatedly on the same asset. Called through the unified HiAPI.ai interface, you describe the parts to keep and the scope of changes in a prompt and get the finished image, making it suitable for turning existing visual assets into multiple versions of material.

Core Highlights

• Iterate on reference images rather than redraw — uses 1–3 reference images as the input base and modifies the specified content while preserving the key details of the original image.
• Built for information-dense images — prompts can describe dense content such as layouts, elements, and text, producing results closer to actual work deliverables.
• Controllable output specifications — resolution tier, aspect ratio, output format, and random seed can all be specified, making it easy to batch-produce assets of uniform specifications.

Capabilities

Multi-reference image editing: accepts 1–3 reference images in common formats such as JPEG, PNG, WEBP, BMP, GIF, and TIFF, with each image no larger than 10 MB; can be used for targeted modifications of scene, style, and clothing.

Long prompts to describe changes: the positive prompt can be up to 18,000 characters and can separately state the parts to keep and the parts to change; the negative prompt independently controls content you do not want to appear.

Layout and text adjustments: for element-dense images, the layout and text content can be adjusted during editing, making it suitable for turning existing posters, interface mockups, and product images into new versions.

Output specification control: choose 1K or 2K resolution, eight aspect ratios, and png or jpeg output formats, with support for a fixed seed to reproduce results.

Automatic prompt optimization: prompt expansion is enabled by default to fill in descriptive details when the prompt is short, and can be turned off to execute exactly as written.

Applicable Scenarios

• Design teams revising posters and banners — input the existing layout image and change instructions to get a new version with updated dimensions, proportions, and copy, ready for direct use in ad delivery and material replacement.
• E-commerce operations transforming product images — use existing product images as references to replace the background, scene, or model clothing, producing multiple sets of display images for detail pages and campaign pages.
• UI concept iteration — input interface sketches or existing mockups, modify the layout and control presentation as prompted, and use them as high-fidelity prototypes for design discussions.
• Multilingual material localization — replace the on-image text and language based on the same original material image, batch-producing versions for different markets for localized ad delivery.

Limitations and Selection Advice

A maximum of 3 reference images, so complex multi-image composition scenarios are limited; SVG input is not supported, and a single reference image must not exceed 10 MB. The readability of layouts and text is affected by typesetting density and language, and dense small text still requires spot-checking. This pipeline only does image-to-image; if you only need to generate an image from text, use the text-to-image pipeline in the same series instead. For batch tasks with high requirements on result stability, it is recommended to fix the seed and keep the prompt version for reproducibility.