让AI画图前先学会查资料、想步骤
现在的AI画图，你给它一句话，它就直接画，遇到需要查资料、多步推理的复杂需求就抓瞎。这篇把画图模型改造成一个会自己规划、会调用搜索工具查资料、再动手画的智能体，整个过程由同一个策略统一指挥，而不是像以前那样各环节各干各的。实验显示，把整个画图流程都交给智能体控制，比固定流程或只控制部分环节的效果都好。它不是你明天就能用上的，但方向很明确：AI画图正在从“会画”走向“会想”。
📄 原文摘要(英文)
Text-to-image (T2I) models can produce visually compelling images, yet they remain limited on open-world tasks that require complex semantic understanding, multi-step reasoning, and the integration of external world knowledge. Existing efforts introduce agent capabilities into image generation, but they either prescribe a fixed workflow or place only a subset of the open-world image generation process under agent control. Consequently, reasoning, tool invocation, and image generation are not coordinated by a single policy. We propose ToolArtist, a fully agentic image generation model obtained by post-training a Unified Multimodal Model (UMM). ToolArtist dynamically orchestrates reasoning, external tool use, and native image generation within one unified policy. During Supervised Fine-Tuning (SFT), we equip a teacher agent with search tools alongside an image-generation tool. We then convert the collected trajectories into a UMM compatible format, where the image-generation tool is concealed while the resulting generated images are retained. During Reinforcement Learning (RL), we develop an agentic RL infrastructure for UMMs and introduce Reason-Act-Draw GRPO (RAD-GRPO), which uses complementary intent and quality rewards to jointly optimize the model. Experiments show that placing the entire open-world image-generation process under an agent policy consistently outperforms approaches with fixed pipelines or only partially agent-controlled components. We release the training data and the complete post-training infrastructure.