We are thrilled to release Qwen-Image, a 20B MMDiT image foundation model that achieves significant advances in complex text rendering and precise image editing. To try the latest model, feel free to visit Qwen Chat and choose “Image Generation”.
The key features include:
Superior Text Rendering: Qwen-Image excels at complex text rendering, including multi-line layouts, paragraph-level semantics, and fine-grained details. It supports both alphabetic languages (e.g., English) and logographic languages (e.g., Chinese) with high fidelity.
Consistent Image Editing: Through our enhanced multi-task training paradigm, Qwen-Image achieves exceptional performance in preserving both semantic meaning and visual realism during editing operations.
Strong Cross-Benchmark Performance: Evaluated on multiple public benchmarks, Qwen-Image consistently outperforms existing models across diverse generation and editing tasks, establishing a strong foundation model for image generation.
We present a comprehensive evaluation of Qwen-Image across multiple public benchmarks, including GenEval, DPG, and OneIG-Bench for general image generation, as well as GEdit, ImgEdit, and GSO for image editing. Qwen-Image achieves state-of-the-art performance on all benchmarks, demonstrating its strong capabilities in both image generation and editing. Furthermore, results on LongText-Bench, ChineseWord, and TextCraft show that it excels in text rendering—particularly in Chinese text generation—outperforming existing state-of-the-art models by a significant margin. This highlights Qwen-Image’s unique position as a leading image generation model that combines broad general capability with exceptional text rendering precision.
One of Qwen-Image’s outstanding capabilities is its ability to achieve high-fidelity text rendering in different scenarios. Let’s take a look at the following Chinese rendering case:
The model not only accurately captures Miyazaki’s anime style, but also features shop signs like “云存储” “云计算” and “云模型” as well as the “千问” on the wine jars, all rendered realistically and accurately with the depth of field. The poses and expressions of the characters are also perfectly preserved.
Let’s look at another example of Chinese rendering:
The model accurately drew the left and right couplets and the horizontal scroll, applied calligraphy effects, and accurately generated the Yueyang Tower in the middle. The blue and white porcelain on the table also looked very realistic.
So, how does the model perform on English?
Let’s look at an English rendering example:
Bookstore window display. A sign displays “New Arrivals This Week”. Below, a shelf tag with the text “Best-Selling Novels Here”. To the side, a colorful poster advertises “Author Meet And Greet on Saturday” with a central portrait of the author. There are four books on the bookshelf, namely “The light between worlds” “When stars are scattered” “The slient patient” “The night circus”
In this example, the model not only accurately outputs “New Arrivals This Week”, but also accurately generates the cover text of four books: “The light between worlds”, “When stars are scattered”, “The slient patient”, and “The night circus”.
Let’s look at a more complex case of English rendering:
A slide featuring artistic, decorative shapes framing neatly arranged textual information styled as an elegant infographic. At the very center, the title “Habits for Emotional Wellbeing” appears clearly, surrounded by a symmetrical floral pattern. On the left upper section, “Practice Mindfulness” appears next to a minimalist lotus flower icon, with the short sentence, “Be present, observe without judging, accept without resisting”. Next, moving downward, “Cultivate Gratitude” is written near an open hand illustration, along with the line, “Appreciate simple joys and acknowledge positivity daily”. Further down, towards bottom-left, “Stay Connected” accompanied by a minimalistic chat bubble icon reads “Build and maintain meaningful relationships to sustain emotional energy”. At bottom right corner, “Prioritize Sleep” is depicted next to a crescent moon illustration, accompanied by the text “Quality sleep benefits both body and mind”. Moving upward along the right side, “Regular Physical Activity” is near a jogging runner icon, stating: “Exercise boosts mood and relieves anxiety”. Finally, at the top right side, appears “Continuous Learning” paired with a book icon, stating “Engage in new skill and knowledge for growth”. The slide layout beautifully balances clarity and artistry, guiding the viewers naturally along each text segment.
In this case, the model needs to generate 6 submodules, each with its own icon, title, and corresponding introductory text. Qwen-Image has completed the layout.
What about smaller text? Let us test it:
A man in a suit is standing in front of the window, looking at the bright moon outside the window. The man is holding a yellowed paper with handwritten words on it: “A lantern moon climbs through the silver night, Unfurling quiet dreams across the sky, Each star a whispered promise wrapped in light, That dawn will bloom, though darkness wanders by.” There is a cute cat on the windowsill.
In this case, the paper is less than one-tenth of the entire image, and the paragraph of text is relatively long, but the model still accurately generates the text on the paper.
What if there are more words? Let’s try a harder case:
You can see that the model has completely generated a complete handwritten paragraph on the glass plate.
What if it’s bilingual? For the same scenario, let’s try this prompt:
一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “Meet Qwen-Image – a powerful image foundation model capable of complex text rendering and precise image editing. 欢迎了解Qwen-Image, 一款强大的图像基础模型，擅长复杂文本渲染与精准图像编辑”
As you can see, the model can switch between two languages at any time when rendering text.
Qwen-Image’s text capabilities make it easy to create posters, such as:
A movie poster. The first row is the movie title, which reads “Imagination Unleashed”. The second row is the movie subtitle, which reads “Enter a world beyond your imagination”. The third row reads “Cast: Qwen-Image”. The fourth row reads “Director: The Collective Imagination of Humanity”. The central visual features a sleek, futuristic computer from which radiant colors, whimsical creatures, and dynamic, swirling patterns explosively emerge, filling the composition with energy, motion, and surreal creativity. The background transitions from dark, cosmic tones into a luminous, dreamlike expanse, evoking a digital fantasy realm. At the bottom edge, the text “Launching in the Cloud, August 2025” appears in bold, modern sans-serif font with a glowing, slightly transparent effect, evoking a high-tech, cinematic aesthetic. The overall style blends sci-fi surrealism with graphic design flair—sharp contrasts, vivid color grading, and layered visual depth—reminiscent of visionary concept art and digital matte painting, 32K resolution, ultra-detailed.
Since we can make posters, of course we can also make PPTs directly. Let’s look at a case of making PPTs in Chinese:
In fact, beyond text processing, Qwen-Image also excels at general image generation, supporting a wide range of artistic styles. From photorealistic scenes to impressionistic paintings, from anime styles to minimalist designs, the model flexibly responds to a wide range of creative prompts, becoming a versatile tool for artists, designers, and storytellers. We will describe these in detail in our technical report.
In terms of image editing, Qwen-Image supports a variety of operations, including style transfer, additions, deletions, detail enhancement, text editing, and character pose adjustment. This allows even ordinary users to easily achieve professional-level image editing. We will describe these in detail in our technical report.
In summary, we hope that Qwen-Image can further promote the development of image generation, lower the technical barriers to visual content creation, and inspire more innovative applications. At the same time, we also look forward to the active participation and feedback of the community to jointly build an open, transparent, and sustainable generative AI ecosystem.
