Abstract

We present Qwen-Image, an image generation foundation model in the Qwenseries that achieves significant advances in complex text rendering and preciseimage editing. To address the challenges of complex text rendering, we design acomprehensive data pipeline that includes large-scale data collection,filtering, annotation, synthesis, and balancing. Moreover, we adopt aprogressive training strategy that starts with non-text-to-text rendering,evolves from simple to complex textual inputs, and gradually scales up toparagraph-level descriptions. This curriculum learning approach substantiallyenhances the model's native text rendering capabilities. As a result,Qwen-Image not only performs exceptionally well in alphabetic languages such asEnglish, but also achieves remarkable progress on more challenging logographiclanguages like Chinese. To enhance image editing consistency, we introduce animproved multi-task training paradigm that incorporates not only traditionaltext-to-image (T2I) and text-image-to-image (TI2I) tasks but alsoimage-to-image (I2I) reconstruction, effectively aligning the latentrepresentations between Qwen2.5-VL and MMDiT. Furthermore, we separately feedthe original image into Qwen2.5-VL and the VAE encoder to obtain semantic andreconstructive representations, respectively. This dual-encoding mechanismenables the editing module to strike a balance between preserving semanticconsistency and maintaining visual fidelity. Qwen-Image achievesstate-of-the-art performance, demonstrating its strong capabilities in bothimage generation and editing across multiple benchmarks.