Qwen VLo, a groundbreaking multimodal large model, has recently been unveiled, marking a significant advancement in image content understanding and generation. This innovative model offers users an unparalleled visual creation experience, taking image generation to new heights.
Building upon the strengths of the original Qwen-VL series, Qwen VLo has undergone a comprehensive upgrade. It not only accurately "understands" the world but also creates high-quality content based on its understanding, truly bridging the gap between perception and generation. Users can now directly experience this new model on the Qwen Chat platform (chat.qwen.ai).
One of the unique features of Qwen VLo is its progressive generation method. When generating images, the model adopts a step-by-step construction strategy from left to right and top to bottom, continuously optimizing and adjusting the predicted content to ensure harmony and consistency in the final result. This generation mechanism not only enhances visual effects but also provides users with a more flexible and controllable creative process.
In terms of content understanding and re-creation, Qwen VLo demonstrates formidable capabilities. Compared to previous multimodal models, Qwen VLo maintains better semantic consistency during the generation process, avoiding issues such as misidentifying cars as other objects or failing to retain key structural features of the original image. For instance, when a user uploads a car photo and requests a color change, Qwen VLo accurately identifies the car model, retains its original structural features, and naturally transitions the color style, resulting in a generation that meets expectations while maintaining a sense of realism.
Furthermore, Qwen VLo supports open instruction editing and modification of generated content. Users can propose various creative instructions through natural language, such as changing the painting style, adding elements, or adjusting the background. The model can flexibly respond to these instructions and generate results that meet user expectations. Whether it's artistic style transfer, scene reconstruction, or detail modification, Qwen VLo can easily handle them all.
It's worth mentioning that Qwen VLo also has multi-language instruction support capabilities. The model supports various language instructions, including Chinese and English, providing a unified and convenient interaction experience for users worldwide. Regardless of the language used, users only need to simply describe their needs, and the model can quickly understand and output the desired results.
In practical applications, Qwen VLo showcases a variety of functions. It can directly generate and modify images, such as replacing backgrounds, adding subjects, or performing style transfers. The model can also complete large-scale modifications based on open instructions, including detection and segmentation visual perception tasks. Moreover, Qwen VLo supports input understanding and generation of multiple images, as well as image detection and annotation functions.
In addition to accepting both text and image inputs, Qwen VLo also supports direct text-to-image generation, including general images and Chinese and English posters. The model uses dynamic resolution training, supporting image generation of any resolution and aspect ratio, allowing users to generate image content that adapts to different scenarios according to their actual needs.
Although Qwen VLo is still in the preview stage and has demonstrated strong capabilities, there are still some shortcomings. For instance, there may be situations where the generation does not conform to facts or is not entirely consistent with the original image. The development team has stated that they will continue to iterate the model, constantly improving its performance and stability.
Experience the Qwen VLo model at chat.qwen.ai.