GITHUB HUGGING FACE MODELSCOPE
我们很高兴开源 Qwen-Image-2.1，这是 Qwen 家族中的一款图像模型，在生成质量、推理效率与成本之间取得平衡。Qwen-Image-2.1 将文生图生成与图像编辑统一在单一模型中，其视觉生成组件仅 7B 参数，并原生支持生成和编辑透明图像。
本次更新带来四项关键改进：
- 紧凑高效：轻量级架构与推理优化在图像质量与计算成本之间取得平衡。
- 原生透明，统一创作与编辑：根据提示词生成普通或透明图像，编辑透明图层，并从照片中提取主体。
- 多样化编辑：最多可使用 10 张参考图像，指定局部编辑，保留人物与产品，并处理各类任务。
- 逼真质感与精致美学：改进的排版、人像光影与精细细节让生成图像更具视觉吸引力。
紧凑高效#
Qwen-Image-2.1 采用轻量级架构。其视觉生成组件包含 32 层单流 DiT 和 7B 参数。尽管体量紧凑，该模型仍能提供出色的图像生成质量。下方的 Qwen-Image-Bench 对比展示了它与其他开源和闭源模型的表现对比。
Qwen-Image-Bench 评测对比
推理效率是另一个重点，尤其是在使用多张输入图像进行编辑时。这一优化来自 混合粒度注意力架构。文本（包括系统前缀和编辑指令）使用 token 级因果掩码，而图像生成使用块级掩码。KV cache 复用 使输入图像和编辑指令可作为静态上下文，在第一步中计算并缓存，从而提升推理效率并降低内存占用。
Qwen-Image-2.1 混合粒度注意力架构
原生透明度，统一创作与编辑#
原生透明度是本次发布的一大新增功能。2025 年 12 月，我们推出了 Qwen-Image-Layered，作为一款支持透明图像生成的专用模型。Qwen-Image-2.1 现将这一能力整合进统一模型，通过提示词决定是输出普通图像还是带透明度通道的图像。
以下是由文本直接生成的透明图像示例：
除了单个主体之外，该模型还能生成由多个元素构成的更复杂的透明图像，为设计和素材创作开辟了更多可能性。
通过统一生成与编辑，Qwen-Image-2.1 还支持直接编辑透明图像。例如，它可以在保留透明背景的同时改变主体的表情。左侧为输入，右侧为编辑结果。
透明图层中的文字同样可以编辑。在下面的示例中，“BLOOM”被替换为“Qwen-Image”。
这一能力也延伸到了真实照片。给定一张 RGB 图像，该模型可以将所需主体提取为带透明度的 RGBA 图层，从而更便于在后续的设计与合成工作中复用元素。
多样化编辑#
Qwen-Image-2.1 在四个方面改进了编辑能力：多参考图像、局部编辑、保真度保持以及任务覆盖范围。
多参考：最多 10 张输入图像#
Qwen-Image-2.1 支持最多 10 张参考图像，可将多个主体和素材组合成一幅协调的构图。在下面的合影示例中，左侧的六张单人肖像被汇聚到一张照片中。
一张由六张人像参考图生成的全家福照片
在虚拟试穿场景中，五个输入——模特、服装、鞋子、包和帽子——可以组合成一套完整的穿搭。
一套由五张参考图生成的完整穿搭
在室内设计场景中，模型可以使用 10 张家具图片生成一个完整的房间布置。
一个由十张家具参考图生成的室内场景
局部编辑：灵活的区域选择#
Qwen-Image-2.1 支持用圆圈、手绘标注和独立掩码来指定编辑应当发生的位置。
第一个示例使用不同颜色的圆圈一次性标出三个区域：“移除蓝色圆圈中的金属手表，将红色圆圈中的头发改为黑色，并将绿色圆圈中的区域替换为灰色短袖亚麻睡衣。”标注后的输入在左侧，结果在右侧。
手绘标注提供了另一种标出区域的方式。在这个示例中，模型在输入图像右侧用白色标出的区域中添加了一名潜水员。
圆圈和手绘标注会遮挡部分原始内容。为保留完整输入，Qwen-Image-2.1 还支持将原始图像和单独的掩码作为两个输入。以下示例要求模型使用这两张图像生成一个骑马牛仔：
模型对掩码指定的区域进行编辑，生成以下结果：
掩码引导编辑结果：一个骑马牛仔
局部编辑还可以支持连续创作。通过在保留场景其余部分的同时进行连续修改，编辑后的图像可以组合成简单的动画，如下方的水豚葫芦兄弟示例所示。
保真度：保留人物与产品#
保真度的改进主要集中在人像身份和产品一致性上。对于人像，模型能更好地保留面部特征，使人物身份在多次编辑中保持更高的一致性。下方每一组左侧为输入，右侧为编辑结果。
对于产品编辑，模型旨在保留文字、纹理和形状，使产品的标志性特征在新图像中保持一致。
任务覆盖范围：全景图、信息图与故事板#
Qwen-Image-2.1 支持广泛的编辑任务，包括全景图、信息图和分镜图生成，在不同应用之间实现了能力平衡。
例如，从这张自拍开始：
用于全景图生成的输入自拍
模型生成了以下全景图：
由自拍生成的全景图
随后可以在可视化工具中从不同视角方向探索这张全景图。
在信息图生成方面，一张模特照片可以被扩展为细节丰富、信息密集的构图。
用于信息图生成的输入模特照片
由模特照片生成的复杂信息图
该模型还可以将三视图角色参考图转化为完整的分镜图，为故事叙述和视觉叙事提供素材。
用于分镜图生成的三视图角色参考图
由角色参考图生成的完整分镜图
逼真的纹理与精致的美学#
Qwen-Image-2.1 进一步提升了视觉质量，尤其关注排版和肖像。文本渲染不仅考虑内容本身，还考虑字体样式、布局及其与整体构图的关系。以下示例展示了不同场景下的文本渲染效果。
文本渲染示例一
文本渲染示例二
文本渲染示例三
文本渲染示例四
对于肖像，改进的光照和精细细节让生成的图像呈现出更逼真的外观。
肖像光照与细节示例一
肖像光照与细节示例二
结论#
凭借紧凑的 7B 视觉生成组件，Qwen-Image-2.1 将图像生成、透明度和广泛的编辑能力集于一个模型之中。更快的推理速度、最多支持 10 张参考图像、灵活的局部编辑，以及对人物和产品更高的保真度，使其成为设计、内容创作、电商和视觉叙事的实用工具。
希望您喜欢用 Qwen-Image-2.1 进行创作！
GITHUB HUGGING FACE MODELSCOPE
We are excited to open-source Qwen-Image-2.1, an image model in the Qwen family that balances generation quality, inference efficiency, and cost. Qwen-Image-2.1 unifies text-to-image generation and image editing in a single model, with just 7B parameters in its visual generation component and native support for generating and editing transparent images.
This update introduces four key improvements:
- Compact and efficient: A lightweight architecture and inference optimizations balance image quality and computational cost.
- Native transparency, unified creation and editing: Generate regular or transparent images from prompts, edit transparent layers, and extract subjects from photos.
- Versatile editing: Use up to 10 reference images, specify local edits, preserve people and products, and handle a wide range of tasks.
- Realistic textures and refined aesthetics: Improved typography, portrait lighting, and fine details make generated images more visually compelling.
Compact and Efficient#
Qwen-Image-2.1 uses a lightweight architecture. Its visual generation component contains 32 Single-Stream DiT layers and 7B parameters. Despite its compact size, the model delivers strong image generation quality. The Qwen-Image-Bench comparison below shows its performance alongside other open-source and closed-source models.
Qwen-Image-Bench evaluation comparison
Inference efficiency is another focus, particularly when editing with multiple input images. This optimization comes from a mixed-granularity attention architecture. Text, including the system prefix and editing instructions, uses a token-level causal mask, while image generation uses a chunk-level mask. KV cache reuse allows input images and editing instructions to serve as static context, computed and cached in the first step to improve inference efficiency and reduce memory usage.
Qwen-Image-2.1 mixed-granularity attention architecture
Native Transparency, Unified Creation and Editing#
Native transparency is a major addition in this release. In December 2025, we introduced Qwen-Image-Layered as a dedicated model supporting transparent image generation. Qwen-Image-2.1 now integrates this capability into a unified model, using the prompt to determine whether to output a regular image or an image with a transparency channel.
Here are examples of transparent images generated directly from text:
Beyond individual subjects, the model can generate more complex transparent images composed of multiple elements, opening up further possibilities for design and asset creation.
By unifying generation and editing, Qwen-Image-2.1 also supports editing transparent images directly. For example, it can change a subject’s expression while retaining the transparent background. The input is on the left and the edited result on the right.
Text within a transparent layer can also be edited. In the following example, “BLOOM” is replaced with “Qwen-Image”.
This capability extends to real photographs. Given an RGB image, the model can extract the desired subject as an RGBA layer with transparency, making it easier to reuse elements in subsequent design and composition work.
Versatile Editing#
Qwen-Image-2.1 improves editing in four areas: multiple reference images, local editing, fidelity preservation, and task coverage.
Multiple References: Up to 10 Input Images#
Qwen-Image-2.1 supports up to 10 reference images, combining multiple subjects and assets into a coherent composition. In the group portrait example below, six individual portraits on the left are brought together in one photograph.
A group photograph generated from six portrait references
For virtual try-on, five inputs—a model, clothing, shoes, a bag, and a hat—can be combined into a complete outfit.
A complete outfit generated from five reference images
For interior design, the model can use 10 images of furnishings to generate a complete room arrangement.
An interior generated from ten furnishing references
Local Editing: Flexible Region Selection#
Qwen-Image-2.1 supports circles, painted annotations, and separate masks to specify where an edit should take place.
The first example uses circles of different colors to identify three regions at once: “Remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas.” The annotated input is on the left, and the result is on the right.
Painted annotations provide another way to identify a region. In this example, the model adds a diver to the area marked in white on the right side of the input image.
Circles and painted annotations obscure some of the original content. To preserve the complete input, Qwen-Image-2.1 also accepts the original image and a separate mask as two inputs. The following example asks the model to generate a cowboy on horseback, using these two images:
The model edits the region specified by the mask, producing the following result:
Mask-guided editing result: a cowboy on horseback
Local editing can also support sequential creation. By making successive changes while preserving the rest of the scene, edited images can be assembled into simple animations, as illustrated by the capybara Calabash Brothers example below.
Fidelity: Preserving People and Products#
Fidelity improvements focus on portrait identity and product consistency. For portraits, the model better preserves facial features so that a person’s identity remains more consistent across edits. Each pair below shows the input on the left and the edited result on the right.
For product editing, the model aims to preserve text, textures, and shape, keeping the product’s defining features consistent in a new image.
Task Coverage: Panoramas, Infographics, and Storyboards#
Qwen-Image-2.1 supports a broad range of editing tasks, including panorama, infographic, and storyboard generation, balancing capabilities across different applications.
For example, starting from this selfie:
Input selfie for panorama generation
The model generates the following panorama:
Panorama generated from the selfie
The panorama can then be explored from different viewing directions in a visualization tool.
For infographic generation, a model photograph can be expanded into a detailed, information-rich composition.
Input model photograph for infographic generation
Complex infographic generated from the model photograph
The model can also turn a three-view character reference into a complete storyboard, providing material for storytelling and visual narratives.
Three-view character reference for storyboard generation
Complete storyboard generated from the character reference
Realistic Textures and Refined Aesthetics#
Qwen-Image-2.1 further improves visual quality, with particular attention to typography and portraits. Text rendering considers not only the content itself, but also type styles, layout, and their relationship to the overall composition. The following examples showcase text rendering across different settings.
Text rendering example one
Text rendering example two
Text rendering example three
Text rendering example four
For portraits, improved lighting and fine details give generated images a more realistic appearance.
Portrait lighting and detail example one
Portrait lighting and detail example two
Conclusion#
With a compact 7B visual generation component, Qwen-Image-2.1 brings image generation, transparency, and a wide range of editing capabilities together in one model. Faster inference, support for up to 10 reference images, flexible local editing, and improved fidelity for people and products make it a practical tool for design, content creation, e-commerce, and visual storytelling.
We hope you enjoy creating with Qwen-Image-2.1!