---
title: "ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation"
description: "ComfyFlow tests whether LLMs can build AIGC pipelines in ComfyUI, the open-source node-based generation platform, in… 44 references."
authors: ["Zhenran Xu", "Yiyu Wang", "Yunxin Li", "Muyang Ye", "Xue Yang", "Kai Chen", "Longyue Wang", "Weihua Luo", "Baotian Hu", "Min Zhang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.140/"
doi: "10.18653/v1/2026.findings-acl.140"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-comfyflow-benchmarking-llms-aigc-workflow/"
---
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# ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation

**Authors:** [Zhenran Xu](https://md.research.elamur.ai/free/en/authors/zhenran-xu/), [Yiyu Wang](https://md.research.elamur.ai/free/en/authors/yiyu-wang/), [Yunxin Li](https://md.research.elamur.ai/free/en/authors/yunxin-li/), [Muyang Ye](https://md.research.elamur.ai/free/en/authors/muyang-ye/), [Xue Yang](https://md.research.elamur.ai/free/en/authors/xue-yang/), [Kai Chen](https://md.research.elamur.ai/free/en/authors/kai-chen/), [Longyue Wang](https://md.research.elamur.ai/free/en/authors/longyue-wang/), [Weihua Luo](https://md.research.elamur.ai/free/en/authors/weihua-luo/), [Baotian Hu](https://md.research.elamur.ai/free/en/authors/baotian-hu/) and [Min Zhang](https://md.research.elamur.ai/free/en/authors/min-zhang/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.140/) · [PDF](https://aclanthology.org/2026.findings-acl.140.pdf) · [DOI 10.18653/v1/2026.findings-acl.140](https://doi.org/10.18653/v1/2026.findings-acl.140) · [OpenAlex](https://openalex.org/W7166870281)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
ComfyFlow tests whether LLMs can build AIGC pipelines in ComfyUI, the open-source node-based generation platform, in place of a human expert. The authors treat the task as domain-specific code generation. A model receives a task description and documentation for candidate nodes, then writes code that converts into an executable DAG on the ComfyUI canvas. To build the resource, they collected community workflows and node definitions. These were cleaned through structural and execution checks and then reviewed manually, producing knowledge bases. From these come a training set of roughly 10K examples and a 400-task test set covering image, video and 3D generation. The evaluation goes beyond simple pass rates. It checks whether outputs are validly formatted, detects hallucinated content in parameters, individual nodes and the overall graph, and assesses adherence to the task request by matching subgraphs and subsequences. Across 13 LLMs, even GPT-5.1 succeeds on only about half the tasks, and quality drops sharply as workflows grow longer. Open-source models post-trained on the data with SFT or RL reach new SOTA. SFT mainly improves instruction following, while RL mainly reduces hallucinations. The released data supports future work on automated workflow planning.

## Key points
- The benchmark has 400 test tasks in 6 categories and 20 subtypes. The training set has 10,677 entries, built from knowledge bases annotating 2,480 nodes and 3,298 workflows.
- Quality control reduced about 27K raw community workflows to about 3.3K verified ones. The node collection went from about 40K raw entries to 6,825 unique nodes after filtering.
- GPT-4o reaches about 96% format validity, but GPT-5.1's pass rate is only 56%. Invalid node selection is the most persistent error type, with a 26% Invalid Node rate even for GPT-4o.
- Difficulty is defined by node count (Easy ≤10, Hard >25). GPT-4o falls from 91% to 35% hallucination pass rate between these levels, while Qwen3-14B-RL falls less (94% → 65%).
- In RL ablations on Qwen3-8B, removing the parameter-fidelity reward lowers format validity and pass rate. Combining SFT+RL balances graph-level F1 and pass rate.

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

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## Cite

Cite as: Zhenran Xu, Yiyu Wang, Yunxin Li, Muyang Ye, Xue Yang, Kai Chen, Longyue Wang, Weihua Luo, Baotian Hu and Min Zhang. 2026. ComfyFlow: Benchmarking LLMs for AIGC 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.

```bibtex
@inproceedings{xu2026comfyflow,
  title = {{ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation}},
  author = {Zhenran Xu, Yiyu Wang, Yunxin Li, Muyang Ye, Xue Yang, Kai Chen, Longyue Wang, Weihua Luo, Baotian Hu and Min Zhang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.140},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.140}
}
```

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*Updated 2 November 2026*