Qwen-Image Technical Report

TL;DR

Qwen-Image is a new foundation model that significantly improves complex text rendering and precise image editing by employing a curriculum learning strategy and multi-task training, excelling in multiple languages and benchmarks.

Contribution

The paper introduces Qwen-Image, a novel image generation and editing model with a comprehensive data pipeline, progressive training, and dual-encoding for enhanced performance and consistency.

Findings

Achieves state-of-the-art results in image generation and editing.

Excels in alphabetic and logographic languages like English and Chinese.

Demonstrates strong performance across multiple benchmarks.

Abstract

We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to-text rendering, evolves from simple to complex textual inputs, and gradually scales up to paragraph-level descriptions. This curriculum learning approach substantially enhances the model's native text rendering capabilities. As a result, Qwen-Image not only performs exceptionally well in alphabetic languages such as English, but also achieves remarkable progress on more challenging logographic languages like Chinese. To enhance image editing consistency, we introduce an improved multi-task training paradigm that incorporates not only traditional text-to-image (T2I) and text-image-to-image (TI2I) tasks but also image-to-image (I2I) reconstruction, effectively aligning the latent representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and reconstructive representations, respectively. This dual-encoding mechanism enables the editing module to strike a balance between preserving semantic consistency and maintaining visual fidelity. Qwen-Image achieves state-of-the-art performance, demonstrating its strong capabilities in both image generation and editing across multiple benchmarks.