On September 21, the Qwen team officially open-sourced the new generation image model Qwen-Image-2.1. This model integrates text-to-image generation, transparent image generation, and various image editing capabilities into a single framework, achieving a new balance between generation quality, inference efficiency, and usage cost with its lightweight visual generation module of only 7B parameters.

QQ20260921-090605.jpg

The visual generation part of Qwen-Image-2.1 adopts a 32-layer Single-Stream DiT structure with only 7B parameters. Through a mixed-granularity attention structure and KV Cache reuse mechanism, the model significantly improves inference efficiency in multi-image input scenarios and effectively controls memory consumption.

Transparent image generation is one of the core highlights of this update. The model can automatically determine whether to output a regular image or an RGBA image with a transparent channel based on the prompt, and supports operations such as modifying the subject's expression and editing text while keeping the transparent background. Users can also input real photos, and the model will automatically extract the subject and output a transparent layer, facilitating subsequent design and compositing.

QQ20260921-090622.jpg

You can modify the subject's expression while keeping the transparent background

QQ20260921-090628.jpg

Text within the transparent layer can also be edited, as shown in the example below, where the text "BLOOM" is changed to "Qwen-Image":

In terms of image editing, Qwen-Image-2.1 supports up to 10 reference images as input, enabling the generation of harmonious scenes by integrating multiple subjects and materials. Local editing supports three methods: selection, brushing, and independent masks, allowing more flexible specification of editing areas. The model has also significantly improved the consistency of human faces and products—after editing, facial features of people and the text and shape of products remain highly realistic.

In addition, the model has made significant improvements in text rendering, lighting on people, and detail rendering, covering various task scenarios such as panoramic images, infographics, and storyboards. Currently, Qwen-Image-2.1 is available for free use by developers and creators on platforms such as GitHub, ModelScope, and Hugging Face.