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After taking advantage of the summer with a bombing of models of the centered on the new open and open source language, and, in some cases, it surpassed the US code/patented code rivals, rivals of closed origin, The “Qwen” Alibaba’s crack team of IA researchers has returned today with the launch of a new highly classified AI Image generator model – Also open source.
Qwen-image stands out in a field full of people with generative image models Due to its emphasis on representing the text with precision within the images – An area where many rivals still fight.
Supporting alphabetic and logographic scripts, the model is particularly expert in administering a complex typography, multiple line designs, semantics at the paragraph level and Bilingual content (for example, English-chino).
In practice, this allows users Generate content such as movie posters, presentation slides, showcase scenes, handwritten poetry and stylized infographics – With a clear text that aligns with its indications.
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QWEN-IMAGE output examples include a wide variety of real world use cases:
- Marketing and brand: Bilingual posters with brand logo, stylistic calligraphy and consistent design reasons
- Presentation design: Mubas of conscious design slides with appropriate titles and images for the subject
- Education: Generation of classroom materials with diagrams and precise instructional text
- Retail and electronic commerce: Showcase scenes where products labels, signage and environmental context should be legible
- Creative content: Hand written poetry, scene, anime style illustration with embedded history text
Users can interact with the model in the Qwen chat Website selecting the “Image Generation” mode in the buttons below the application entry field.
However, my brief initial evidence revealed that the text and the early adhesion were not remarkably better than Midjourney, the popular patented image generator of the US company of the same name. My session through the chat qwen produced multiple errors in the rapid understanding and fidelity of the text, for my disappointment, even after repeated attempts and a new writing:
However, Midjourney only offers a limited number of free generations and requires subscriptions for more, compared to Qwen Image, which, thanks to its open source licenses and weights, published in Hugged faceIt can be adopted by any third -party company or supplier for free.
Licenses and availability
Qwen-image is distributed under the Apache 2.0 licenseallowing commercial and non -commercial use, redistribution and modification, although the attribution and inclusion of the license text are necessary for derived works.
This can make it attractive to companies that seek a tool for generating open source images to make internal or external collateral such as flyers, advertisements, notices, newsletters and other digital communications.
But the fact that model training data remains a very protected secret – As with most other main generators of AI – You can group some companies about the idea of using it.
Qwen, unlike Adobe Firefly either GPT-4o generation of Native Images of OpenAi, For example, does not offer compensation for commercial uses of your product (That is, if a user is sued for copyright, adobe and openai will help support them in court).
The associated model and assets, including demonstration notebooks, evaluation tools and tight scripts, are available through multiple repositories:
In addition, a live evaluation portal called AI Arena allows users to compare generations of image in rounds by peers, contributing to a public classification table of Eloo style.
Training and development
Behind Qwen-Image’s performance there is a Extensive training process based on progressive learning, alignment of multimodal tasks and aggressive data healingAccording to him Technical Document The research team published today.
The training corpus includes billions of image text pairs from four domains: natural images, human portraits, artistic and design content (such as posters and designs of UI) and synthetic data focused on the text. QWEN team did not specify the size of the training data corpusApart from “billions of image text pairs.” They provided a breakdown of the approximate percentage of each category of content that included:
- Nature: ~ 55%
- Design (UI, posters, art): ~ 27%
- People (portraits, human activity): ~ 13%
- Synthetic text representation data: ~ 5%
In particular, Qwen emphasizes that all synthetic data were generated internally, and no images created by other AI models were used. Despite the detailed stages of healing and filtering described, The documentation does not clarify whether any of the data was licensed or extracted from public data or owners sets.
Unlike many generative models that exclude synthetic text due to noise risks, the QWEN image uses strictly controlled synthetic representation pipes to improve the coverage of the characters, especially for low frequency characters in Chinese.
A curricular style strategy is used: the The model begins with simple subtitled images and non -text contentThen advance to text scenarios sensitive to design, representation of mixed language and dense paragraphs. This It is shown that gradual exposure helps the model to generalize between scripts and types of format.
Qwen-image integrates three key modules:
- QWen2.5-VLThe multimodal language model extracts contextual meaning and guides the generation through the system indications.
- VAE CODER/DECODIFIERTrained in high -resolution documents and real world designs, handles detailed visual representations, especially small or dense text.
- MmditThe spine of the diffusion model coordinates joint learning through image and text modalities. A new MSROPE system (multimodal scalable rotating positional) improves spatial alignment between tokens.
Together, these components allow qwen-image to function effectively in tasks that imply the understanding of images, generation and precise edition.
Performance Points
Qwen-Image was evaluated with several public reference points:
- Gineval and DPG For the consistency of monitoring and object attribute
- Bench Oneig and Tiif For compositional reasoning and design fidelity
- CVTG-2K, Chinese wordand Long -term banks For text representation, especially in multilingual contexts
In almost all cases, Qwen-Image coincides or exceeds existing code models closed as GPT Image 1 [High]Seedream 3.0 and Flux.1 Kontext [Pro]. In particular, its performance in the representation of Chinese text was significantly better than all comparative systems.
In the public classification table of the AI sand, based on more than 10,000 human comparisons by pairs, Qwen-Image occupies third place in general and is the open source model.
Implications for business technical decision makers
For business teams that manage complex multimodal workflows, Qwen-Image has several functional advantages that are aligned with the operational needs of different roles.
Those who handle the life cycle of the models in the vision language, from training to implementation, Will Find value in the quality of consistent output of Qwen-Image and its components ready for integration. Open source nature reduces license costs, while modular architecture (QWEN2.5-VL + VAE + MMDIT) facilitates adaptation to personalized data sets or fine adjustment for specific domain outputs.
He Curriculum training data and clear reference results help teams evaluate physical aptitude for their purpose. Whether they implement marketing images, representations of documents or graphics of electronic commerce products, the QWEN image allows rapid experimentation without proprietary restrictions.
Engineers The task of building AI pipes or implementing models in distributed systems will appreciate the detailed infrastructure documentation. The model has been trained using a producer consumer architecture, admits a scalable multiple resolution processing (256p to 1328p), and is built to function with Megatron-LM and tensioning parallelism. This It makes Qwen-Image a candidate for implementation in hybrid cloud environments where reliability and performance matter.
In addition, support for image editing workflows in image (TI2I) and the specific indications of the task allow its use in real or interactive applications.
Professionals focused on data ingestion, validation and transformation You can use QWEN-image as a tool to generate synthetic data sets to train or increase computer vision models. Its ability to generate high -resolution images with embedded multilingual annotations can improve performance in OCR analysis tasks, object detection or design.
Since Qwen-Image was Also trained to avoid artifacts such as QR codesDistorted text and water brands, offers a higher quality synthetic entry than many public models, which helps business teams to preserve the integrity of the training set.
Looking for comments and opportunities to collaborate
Qwen’s team emphasizes community opening and collaboration in the launch of the model.
Developers are encouraged to evaluate and refine QWen-Image, offer extraction requests and participate in the evaluation classification. Feedback on text representation, the edition of fidelity and multilingual use cases will shape future iterations.
With an established objective to “reduce technical barriers for the creation of visual content”, the team expects Qwen-Image to serve not only as a model, but as a basis for future research and practical implementation in all industries.
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