ComfyUI-R1: Exploring Reasoning Models for Workflow Generation
Authors: name: Zhenran Xu, name: Yiyu Wang, name: Xue Yang, name: Longyue Wang, name: Weihua Luo, name: Kaifu Zhang, name: Baotian Hu, name: Min Zhang

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
dataset of 4K workflows, 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, significantly surpassing prior
state-of-the-art methods that employ leading closed-source models such as
GPT-4o and Claude series. Further analysis highlights the critical role of the
reasoning process and the advantage of transforming workflows into code.
Qualitative comparison reveals our strength in synthesizing intricate workflows
with diverse nodes, underscoring the potential of long CoT reasoning in AI art
creation.