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home / papers / 2026 / ComfyUI-R1: Exploring Reasoning Models for Workflow Generation

ComfyUI-R1: Exploring Reasoning Models for Workflow Generation

Paper · 2026 · Zhenran Xu, Yiyu Wang, Xue Yang et al.

Summary

ComfyUI is a node-based platform for building image and video generation pipelines. Assembling a working workflow there takes real expertise, because the platform has thousands of community components with complex dependencies. Earlier automation attempts prompted proprietary LLMs or multi-agent setups to emit JSON, and they often produced broken graphs or nonexistent nodes. Most of them also handled only text-to-image tasks.

The authors present ComfyUI-R1, which they describe as the first reasoning-oriented model built specifically for this job. They collected workflows and node documentation from community sites into knowledge bases spanning image, video and 3D tasks. From these they synthesized long chain-of-thought traces covering node retrieval, node selection, step-by-step planning and finally Python-like code that encodes the workflow.

Training on Qwen2.5-Coder proceeds in two stages. A cold-start supervised fine-tuning phase comes first, followed by GRPO reinforcement learning. The RL stage uses a hybrid reward: invalid format, non-DAG structure or hallucinated nodes are penalized, and only well-formed outputs earn credit for correct node choice.

The resulting 7B model beats GPT-4o- and Claude-based systems. This suggests that small open models with targeted post-training can lower the entry barrier to creative AI tools.

Key points

  • Base model: Qwen2.5-Coder 7B. Format validity rose from 41% for the original model to 97% after both training stages, with strong node-level and graph-level F1.
  • On a 42-task subset of ComfyBench (text-to-image, image editing, text-to-video, image-to-video), ComfyUI-R1 reached the best pass and solve rates. It outperformed ComfyAgent, o3-mini and Gemini-2.5-pro.
  • For ComfyBench, candidate nodes come from the top 3 workflows retrieved from the knowledge base using text-embedding-3-small.
  • Ablations indicate three things help: the explicit reasoning step, the fine-grained reward components, and representing workflows as code instead of JSON.
  • The model handled nodes introduced after training, such as API nodes released 6 May 2025. Its main limitation is that it relies on default node parameters. Code is available at GitHub AIDC-AI/ComfyUI-Copilot.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Harbin Institute of TechnologyHarbin · China158 works · 28,841 citations🏢Alibaba GroupHangzhou · China168 works · 15,386 citations

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Zhenran Xu, Yiyu Wang, Xue Yang, Longyue Wang, Weihua Luo, Kaifu Zhang, Baotian Hu and Min Zhang. 2026. ComfyUI-R1: Exploring Reasoning Models for Workflow Generation. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.146

@inproceedings{xu2026comfyui,
  title = {{ComfyUI-R1: Exploring Reasoning Models for Workflow Generation}},
  author = {Zhenran Xu, Yiyu Wang, Xue Yang, Longyue Wang, Weihua Luo, Kaifu Zhang, Baotian Hu and Min Zhang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.146},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.146}
}

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