Alibaba upgrades its Qwen image model with visual and semantic image editing

Two editing modes for different workflows

For semantic editing, Alibaba demonstrates how the model can create new IP content featuring its Capybara mascot. Even when most pixels change, the character remains recognizable.

Other use cases include generating new perspectives with 90- or 180-degree object rotations and using style transfer for avatar creation, such as converting portraits into Studio Ghibli-style images.

Qwen Image Edit can also add signs with realistic reflections, remove stray hairs, change letter colors, and edit backgrounds or clothing.

Bilingual text editing with step-by-step correction

One of Qwen Image Edit's main strengths is its ability to edit text in both Chinese and English. The system can add, remove, or change text directly in images while preserving the original font, size, and style.

Users can draw bounding boxes around incorrect or unwanted text. The model then updates those marked areas. While it sometimes struggles with rare or unusual characters like "稽," users can make step-by-step edits, marking specific spots and having the model refine the results until they are satisfied.

Alibaba says Qwen Image Edit delivers state-of-the-art performance on public image editing benchmarks, though it hasn't shared specific numbers. The model is available through Qwen Chat's "Image Editing" feature and can also be found on Github, Hugging Face, and Modelscope.

Qwen Image Edit reflects just how quickly targeted image editing and text rendering are advancing. Until recently, it was difficult for AI to change only specific parts of an image without disrupting everything else.

Black Forest Labs has also entered the space with Flux.1 Context, a model that combines text-to-image generation and image editing. But Flux.1 Context still shows visible artifacts in longer editing chains and sometimes has trouble handling prompts accurately.