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cs.CV2026

Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation

Zekai Zhang, Jiahao Li, Jie Zhang +18

While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or dependent on up-to-date knowl…

cs.CV2026

Qwen-Image-2.0-RL Technical Report

Yixian Xu, Kaiyuan Gao, Yuxiang Chen +25

We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual qu…

cs.CV2026

Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation

Niantong Li, Guangzheng Hu, Weixu Qiao +35

Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no…

cs.CV2026

Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation

Jie Zhang, Xiaoyue Chen, Anzhe Chen +36

We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically ground…

cs.CV2026

Qwen-Image-Flash: Beyond Objective Design

Tianhe Wu, Kun Yan, Zikai Zhou +21

Few-step distillation has become an effective strategy for accelerating advanced visual generative models, yet prior work has largely focused on distillation objectives. In this wo…

cs.CV2026

Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration

Xinghua Huang, Zhixiong Yang, Chen Wu +6

A fundamental difficulty in all-in-one blind image restoration is that degradation is usually treated as an implicit factor hidden in degraded-to-clean mapping, rather than as an e…