Kaiyuan Gao

Bridging the Context Gap in T2I models, Qwen-Image-Agent achieves state-of-the-art performance by intelligently constructing context from user input and external sources.

A novel reward system boosts Qwen-Image-2.0's performance, achieving a 2.61 point increase in overall quality and significant gains in both text-to-image and image editing tasks.

Language-driven video generation in Qwen-RobotWorld achieves unprecedented accuracy in predicting robotic actions, outperforming existing models across key benchmarks.

Rethinking few-step distillation reveals that the training pipeline's organization is as crucial as the distillation objectives themselves.

Existing text-to-image benchmarks miss the mark on real-world artistic creation, but Qwen-Image-Bench finally provides a creator-centric evaluation that reliably distinguishes state-of-the-art models.

Quotient-space diffusion elegantly sidesteps the need to learn symmetry transformations, leading to more efficient and accurate generative models for systems with inherent symmetries.