Text-to-image diffusion models have revolutionized generative AI, enabling high-quality and photorealistic image synthesis. However, their practical deployment remains hindered by several limitations: sensitivity to prompt phrasing, ambiguity in semantic interpretation (e.g., ``mouse" as animal vs. a computer peripheral), artifacts such as distorted anatomy, and the need for carefully engineered input prompts. Existing methods often require additional training and offer limited controllability, restricting their adaptability in real-world applications. We introduce Self-Improving Diffusion Agent (SIDiffAgent), a training-free agentic framework that leverages the Qwen family of models (Qwen-VL, Qwen-Image, Qwen-Edit, Qwen-Embedding) to address these challenges. SIDiffAgent autonomously manages prompt engineering, detects and corrects poor generations, and performs fine-grained artifact removal, yielding more reliable and consistent outputs. It further incorporates iterative self-improvement by storing a memory of previous experiences in a database. This database of past experiences is then used to inject prompt-based guidance at each stage of the agentic pipeline. \modelour achieved an average VQA score of 0.884 on GenAIBench, significantly outperforming open-source, proprietary models and agentic methods. We will publicly release our code upon acceptance.

翻译：文本到图像扩散模型已彻底改变了生成式人工智能，实现了高质量且逼真的图像合成。然而，其实际部署仍受到若干限制的阻碍：对提示词表述的敏感性、语义解释的模糊性（例如，“mouse”作为动物与计算机外围设备）、诸如扭曲解剖结构等伪影，以及对精心设计的输入提示的需求。现有方法通常需要额外训练且可控性有限，限制了其在现实应用中的适应性。我们引入了自改进扩散智能体（SIDiffAgent），这是一种免训练的智能体框架，利用Qwen系列模型（Qwen-VL、Qwen-Image、Qwen-Edit、Qwen-Embedding）来解决这些挑战。SIDiffAgent自主管理提示工程，检测并纠正不良生成结果，执行细粒度伪影去除，从而产生更可靠和一致的输出。它通过将先前经验存储在数据库中，进一步融入了迭代式自改进机制。该经验数据库随后被用于在智能体流程的每个阶段注入基于提示的引导。\modelour在GenAIBench上取得了平均0.884的VQA分数，显著优于开源模型、专有模型以及其他智能体方法。我们将在论文被接受后公开代码。