This academic paper introduces Qwen-Image, an open-source model designed for generating high-quality images from text. It details the multi-stage data filtering pipeline used to curate a diverse and high-quality training dataset, categorized into Nature, Design, People, and Synthetic Data. The paper also explains the Multimodal Scalable RoPE (MSRoPE) encoding strategy, which improves image resolution scaling and text-image alignment within the model's architecture. Furthermore, the text describes the distributed training optimizations and reinforcement learning strategies, like DPO and GRPO, employed to enhance model performance. Finally, Qwen-Image is showcased as a strong competitor to leading closed-source models in image generation, particularly excelling in Chinese text rendering and complex instruction following.
Source: https://arxiv.org/pdf/2508.02324
