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

Source: https://huggingface.co/papers/2606.26907 Published on Jun 25

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Abstract

A unified agentic framework called Qwen-Image-Agent is proposed to address the context gap in text-to-image generation by progressively constructing complete generation context through planning, reasoning, searching, and memory mechanisms.

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 knowledge. We identify this challenge as theContext Gap: the mismatch between the user context and the sufficient generation context for T2I models. To bridge this gap, we propose Qwen-Image-Agent, a unifiedagentic frameworkthat integratesplan,reason,search,memoryand feedback in a context-centric manner. Qwen-Image-Agent treats user input as partial context and progressively constructs the generation context throughContext-Aware PlanningandContext Grounding. Specifically,Context-Aware Planningidentifies missing context andplans how it should be acquired and used, whileContext Groundinggathers this context fromreason,search,memory, and feedback. To evaluate agentic image generation, we further introduceImage Agent Bench(IA-Bench), a benchmark covering four coreimage agent capabilities:Plan,Reason,Search, andMemory. Experiments on IA-Bench, Mindbench and WISE-Verified show that Qwen-Image-Agent outperforms strong baselines and achieves state-of-the-art performance.

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