@inproceedings{xu-etal-2026-comfyui,
    title = "{C}omfy{UI}-R1: Exploring Reasoning Models for Workflow Generation",
    author = "Xu, Zhenran  and
      Wang, Yiyu  and
      Yangxue  and
      Wang, Longyue  and
      Luo, Weihua  and
      Zhang, Kaifu  and
      Hu, Baotian  and
      Zhang, Min",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.146/",
    pages = "2999--3013",
    ISBN = "979-8-89176-395-1",
    abstract = "AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated knowledge bases, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97{\%} format validity rate, along with high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Qualitative analysis further highlights our model{'}s strength in synthesizing intricate workflows with diverse nodes, aligning with human instructions, and generalizing to newly introduced nodes, underscoring the potential of long CoT reasoning in AI art creation."
}